Signatures and determinants for diagnosing infections and methods of use thereof

ABSTRACT

Antibiotics (Abx) are the world&#39;s most misused drugs. Antibiotics misuse occurs when the drug is administered in case of a non-bacterial infection (such as a viral infection) for which it is ineffective. Overall, it is estimated that 40-70% of the worldwide Abx courses are mis-prescribed. The financial and health consequences of Abx over-prescription include the direct cost of the drugs, as well as the indirect costs of their side effects, which are estimated at &gt;$15 billion annually. Furthermore, over-prescription directly causes the emergence of Abx-resistant strains of bacteria, which are recognized as one of the major threats to public health today. This generates an immediate need for reliable diagnostics to assist physicians in correct Abx prescription, especially at the point-of-care (POC) where most Abx are prescribed. Accordingly, some aspects of the present invention provide methods using biomarkers for rapidly detecting the source of infection and administrating the appropriate treatment.

RELATED APPLICATIONS

This application is a National Phase of PCT Patent Application No.PCT/EP2013/052619 having International filing date of Feb. 8, 2013,which claims the benefit of priority under USC §119(e) of U.S.Provisional Patent Application Nos. 61/596,950 filed on Feb. 9, 2012 and61/652,631 filed on May 29, 2012. The contents of the above applicationsare all incorporated by reference as if fully set forth herein in theirentirety.

FIELD OF THE INVENTION

The present invention, in some embodiments thereof, relates generally tothe identification of biological signatures and determinants associatedwith bacterial and viral infections and methods of using such biologicalsignatures in the screening diagnosis, therapy, and monitoring ofinfection.

BACKGROUND OF THE INVENTION

Antibiotics (Abx) are the world's most prescribed class of drugs with a25-30 billion $US global market. Abx are also the world's most misuseddrug with a significant fraction of all drugs (40-70%) being wronglyprescribed (Linder, J. A. and R. S. Stafford 2001; Scott, J. G. and D.Cohen, et al. 2001; Davey, P. and E. Brown, et al 2006; Cadieux, G. andR. Tamblyn, et al. 2007; Pulcini, C. and E. Cua, et al. 2007) (“CDC—GetSmart: Fast Facts About Antibiotic Resistance” 2011).

One type of Abx misuse is when the drug is administered in case of anon-bacterial disease, such as a viral infection, for which Abx isineffective. For example, according to the USA center for diseasecontrol and prevention CDC, over 60 Million wrong Abx prescriptions aregiven annually to treat flu in the US. The health-care and economicconsequences of the Abx over-prescription include: (i) the cost ofantibiotics that are unnecessarily prescribed globally, estimatedat >$10 billion annually; (ii) side effects resulting from unnecessaryAbx treatment are reducing quality of healthcare, causing complicationsand prolonged hospitalization (e.g. allergic reactions, Abx associateddiarrhea, intestinal yeast etc.) and (iii) the emergence of resistantstrains of bacteria as a result of the overuse (the CDC has declared therise in antibiotic resistance of bacteria as “one of the world's mostpressing health problems in the 21^(st) century” (Arias, C. A. and B. E.Murray 2009; “CDC—About Antimicrobial Resistance” 2011)).

Antibiotics under-prescription is not uncommon either. For example up to15% of adult bacterial pneumonia hospitalized patients in the US receivedelayed or no Abx treatment, even though in these instances earlytreatment can save lives and reduce complications (Houck, P. M. and D.W. Bratzler, et al 2002).

Technologies for infectious disease diagnostics have the potential toreduce the associated health and financial burden associated with Abxmisuse. Ideally, such a technology should: (i) accurately differentiatebetween a bacterial and viral infections; (ii) be rapid (withinminutes); (iii) be able to differentiate between pathogenic andnon-pathogenic bacteria that are part of the body's natural flora; (iv)differentiate between mixed co-infections and pure viral infections and(v) be applicable in cases where the pathogen is inaccessible (e.g.sinusitis, pneumonia, otitis-media, bronchitis, etc).

Current solutions (such as culture, PCR and immunoassays) do not fulfillall these requirements: (i) Some of the assays yield poor diagnosticaccuracy (e.g. low sensitivity or specificity)(Uyeki et al. 2009), andare restricted to a limited set of bacterial or viral strains; (ii) theyoften require hours to days; (iii) they do not distinguish betweenpathogenic and non-pathogenic bacteria (Del Mar, C 1992), thus leadingto false positives; (iv) they often fail to distinguish between a mixedand a pure viral infections and (v) they require direct sampling of theinfection site in which traces of the disease causing agent are searchedfor, thus prohibiting the diagnosis in cases where the pathogen residesin an inaccessible tissue, which is often the case.

Consequentially, there still a diagnostic gap, which in turn often leadsphysicians to either over-prescribe Abx (the “Just-in-case-approach”),or under-prescribe Abx (the “Wait-and-see-approach”) (Little, P. S. andI. Williamson 1994; Little, P. 2005; Spiro, D. M. and K. Y. Tay, et al2006), both of which have far reaching health and financialconsequences.

Accordingly, a need exists for a rapid method that accuratelydifferentiates between bacterial, viral, mixed and non-infectiousdisease patients that addresses these challenges.

SUMMARY OF THE INVENTION

The present invention, in some embodiments thereof, is based on theidentification of signatures and determinants associated with bacterial,viral and mixed (i.e., bacterial and viral co-infections) infections,patients with a non-infectious disease and healthy subjects. The methodsof the invention allow for the identification of type of infection asubject is suffering from, which in turn allows for the selection of anappropriate treatment regimen. Various embodiments of the inventionaddress limitations of current diagnostic solutions by: (i) allowingaccurate diagnostics on a broad range of pathogens; (ii) enabling rapiddiagnosis (within minutes); (iii) insensitivity to the presence ofnon-pathogenic bacteria and viruses (thus reducing the problem offalse-positive); (iv) providing means for distinguishing between mixedfrom pure viral infections, and (v) eliminating the need for directsampling of the pathogen, thus enabling diagnosis of inaccessibleinfections. Thus, some methods of the invention allow for the selectionof subjects for whom antibiotic treatment is desired and preventunnecessary antibiotic treatment of subjects having only a viralinfection or a non-infectious disease. Some methods of the inventionalso allow for the selection of subjects for whom anti-viral treatmentis advantageous.

To develop and validate various aspects of the invention, the inventorsconducted a large prospective multi-center clinical trial enrolling 655hospital patients with different types of infections as well as controls(patients with a non-infectious disease and healthy individuals).

The inventors then performed meticulous molecular and biochemicalexperimentation and measured the levels of over 570 polypeptides andother physiological determinants in these patients using quantitativeassays. They found that most determinants were not indicative of theunderlying infection type (e.g. bacterial, viral mixed andnon-infectious disease). Moreover, even determinants with awell-established immunological role in the host response to infectionfailed to robustly distinguish between patients with differentunderlying infection types. Diverging from this norm were a few uniquedeterminants, which the inventors were able to identify, that were ableto differentiate between various types of infections.

In various aspects the invention provides methods of ruling out abacterial infection in a subject by measuring the polypeptideconcentration of TRAIL in a subject derived sample; and ruling out abacterial infection for the subject if the polypeptide concentration ofTRAIL determined is higher than a pre-determined first threshold value.Optionally, the method further includes ruling in a viral infection inthe subject if the polypeptide concentration of TRAIL is higher than apre-determined second threshold value.

In another aspect the invention provides a method of ruling out a viralinfection in a subject measuring the polypeptide concentration of TRAILin a subject derived sample; and ruling out a viral infection for thesubject if the polypeptide concentration of TRAIL determined is lowerthan a pre-determined first threshold value. Optionally, the methodfurther includes ruling in a bacterial infection in the subject if thepolypeptide concentration of TRAIL determined in step (a) is lower thana pre-determined second threshold value.

In a further aspect the invention provides a method of ruling in abacterial infection in a subject by measuring the polypeptideconcentration of TRAIL in a subject derived sample ruling in a bacterialinfection for the subject if the polypeptide concentration of TRAIL islower than a pre-determined first threshold value.

In another aspects the invention provides a method of ruling in a viralinfection in a subject by measuring the polypeptide concentration ofTRAIL in a subject derived sample; and ruling in a viral infection forthe subject if the polypeptide concentration of TRAIL is higher than apre-determined first threshold value.

In various aspects the invention includes a method of distinguishingbetween a bacterial infection and a viral infection in a subject bymeasuring the polypeptide concentration of TRAIL and CRP in a subjectderived sample, applying a pre-determined mathematical function on theconcentrations of TRAIL and CRP to compute a score and comparing thescore to a predetermined reference value.

In another aspect, the invention provides a method of distinguishingbetween a bacterial or mixed infection, and a viral infection in asubject by measuring the polypeptide concentration of TRAIL and CRP in asubject derived sample, applying a pre-determined mathematical functionon the concentrations of TRAIL and CRP to compute a score and comparingthe score to a predetermined reference value.

In various embodiments any of the above described methods furtherincludes measuring the polypeptide concentration of one or morepolypeptide selected from the group consisting of SAA, PCT, B2M Mac-2BP,IL1RA and IP10, applying a pre-determined mathematical function on theconcentrations of the polypeptide concentration measure to compute ascore, comparing the score to a predetermined reference value.Specifically in some embodiments TRAIL, CRP and SAA are measured; TRAIL,CRP and IP10 are measured.

TRAIL, CRP and PCT are measured; TRAIL, CRP and IL1RA are measured;TRAIL, CRP and B2M are measured; TRAIL, CRP and Mac-2BP are measured;TRAIL, CRP, SAA and PCT are measured; TRAIL, CRP, Mac-2BP and SAA aremeasured; TRAIL, CRP, SAA and IP10 are measured; TRAIL, CRP, SAA andIL1RA are measured; TRAIL, CRP, SAA, PCT and IP10 are measured; TRAIL,CRP, SAA, PCT and IL1RA are measured; or TRAIL, CRP, SAA, IP10 and IL1RAare measured.

In a further aspect the invention includes method of providing atreatment recommendation i.e., selecting a treatment regimen for asubject by measuring the polypeptide concentration of TRAIL in a subjectderived sample; and recommending that the subject receives an antibiotictreatment if polypeptide concentration of TRAIL is lower than apre-determined threshold value; recommending that the patient does notreceive an antibiotic treatment if the polypeptide concentration ofTRAIL is higher than a pre-determined threshold value; or recommendingthat the patient receive an anti-viral treatment if the polypeptideconcentration of TRAIL determined in step (a) is higher than apre-determined threshold value.

In another aspect the invention includes a method of providing atreatment recommendation for a subject by identifying the type infection(i.e., bacterial, viral, mixed infection or no infection) in the subjectaccording to the method of any of the disclosed methods and recommendingthat the subject receive an antibiotic treatment if the subject isidentified as having bacterial infection or a mixed infection; or ananti-viral treatment is if the subject is identified as having a viralinfection.

In yet another aspect the invention provides a method of providing adiagnostic test recommendation for a subject by measuring thepolypeptide concentration of TRAIL in a subject derived sample; andrecommending testing the sample for a bacteria if the polypeptideconcentration of TRAIL is lower than a pre-determined threshold value;or recommending testing the sample for a virus if the polypeptideconcentration of TRAIL is higher than a pre-determined threshold value.

In a further aspect the invention includes method of providing adiagnostic test recommendation for a subject by identifying theinfection type (i.e., bacterial, viral, mixed infection or no infection)in the subject according to any of the disclosed methods.

Recommending a test to determine the source of the bacterial infectionif the subject is identified as having a bacterial infection or a mixedinfection; or a test to determine the source of the viral infection ifthe subject is identified as having a viral infection.

In various aspects any of the above methods further includes measuringone or more of the following DETERMINANTS IL1RA, IP10, Mac-2BP, B2M,BCA-1, CHI3L1, Eotaxin, IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C,EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A,CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162,HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1, IgG non-specificbound molecules, IL1, I-TAC; IFITM3, IFIT3, EIF4B, IFIT1, LOC26010,MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A, IFITM1, IL7.

CRP, SAA, TREM-1, PCT, IL-8, TREM-1 and IL6; Age, absolute neutrophilcount (ANC), absolute lymphocyte count (ALC), neutrophil % (Neu (%)),lymphocyte % (Lym (%)), monocyte % (Mono (%)), Maximal temperature, Timefrom symptoms, Creatinine (Cr), Potassium (K), Pulse and Urea.

In another aspect the invention provide a method of distinguishingbetween a subject having an infectious disease and one having anon-infectious disease. For example, in one embodiment the an infectiousdisease is ruled out in a subject measuring the polypeptideconcentration of one or more polypeptides including TRAIL, IP10, IL1Raor Mac-2BP in a subject derived sample; applying a pre-determinedmathematical function on the concentrations of the polypeptides measuredto compute a score, comparing the score to a predetermined referencevalue. Optionally, the polypeptide concentration of one or morepolypeptides including SAA, CRP, IL6, IL8, and PCT, TREM-1 are measured.

In various aspects the method distinguishes a virally infected subjectfrom either a subject with non-infectious disease or a healthy subject;a bacterially infected subject, from either a subject withnon-infectious disease or a healthy subject; a subject with aninfectious disease from either a subject with an non-infectious diseaseor a healthy subject; a bacterially infected subject from a virallyinfected subject; a mixed infected subject from a virally infectedsubject; a mixed infected subject from a bacterially infected subjectand a bacterially or mixed infected and subject from a virally infectedsubject.

These methods include measuring the levels of a first DETERMINANTincluding TRAIL, IL1RA, IP10, Mac-2BP, B2M, BCA-1, CHI3L1, Eotaxin,IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1,RTN3, CD112, CD134, CD182, CD231, CD235A, CD335, CD337, CD45, CD49D,CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B,SELI, SPINT2, SSEA1, IgG non-specific bound molecules, IL1, I-TAC andTNFR1 in a sample from the subject and measuring the levels of a secondDETERMINANT including TRAIL, IL1RA, IP10, Mac-2BP, B2M, BCA-1, CHI3L1,Eotaxin, IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15,RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A, CD335, CD337, CD45,CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1,RAP1B, SELI, SPINT2, SSEA1, IgG non-specific bound molecules, IL1, I-TACTNFR1; IFITM3, IFIT3, EIF4B, IFIT1, LOC26010, MBOAT2, MX1, OAS2, RSAD2,ADIPOR1, CD15, CD8A, IFITM1, IL7; CRP, SAA, TREM-1, PCT, IL-8, TREM-1and IL6; Age, absolute neutrophil count (ANC), absolute lymphocyte count(ALC), neutrophil % (Neu (%)), lymphocyte % (Lym (%)), monocyte % (Mono(%)), Maximal temperature, Time from symptoms, Creatinine (Cr),Potassium (K), Pulse and Urea and comparing the levels of the first andsecond DETERMINANTS to a reference value thereby identifying the type ofinfection in the subject wherein the measurement of the secondDETERMINANT increases the accuracy of the identification of the type ofinfection over the measurement of the first DETERMINANT.

Optionally, further includes measuring the level of a one or moreadditional DETERMINANTS including: TRAIL, IL1RA, IP10, Mac-2BP, B2M,BCA-1, CHI3L1, Eotaxin, IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C,EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A,CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162,HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1, IgG non-specificbound molecules, IL1, I-TAC TNFR1; IFITM3, IFIT3, EIF4B, IFIT1,LOC26010, MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A, IFITM1, IL7;CRP, SAA, TREM-1, PCT, IL-8, TREM-1 and IL6; Age, absolute neutrophilcount (ANC), absolute lymphocyte count (ALC), neutrophil % (Neu (%)),lymphocyte % (Lym (%)), monocyte % (Mono (%)), Maximal temperature, Timefrom symptoms, Creatinine (Cr), Potassium (K), Pulse and Urea; whereinthe measurement of the additional DETERMINANTS increases the accuracy ofthe identification of the type of infection over the measurement of thefirst and second DETERMINANTS. In one aspect the method distinguishes abacterially infected subject from a virally infected subject bymeasuring one or more DETERMINANTS selected from B2M, BCA-1, CHI3L1,Eotaxin, IL1RA, IP10, MCP, Mac-2BP, TRAIL, CD62L and VEGFR2 are measuredand one or more DETERMINANTS selected from the group consisting of CRP,TREM-1, SAA, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym (%), Mono (%),Maximal temperature, Time from symptoms, Age, Creatinine (Cr), Potassium(K), Pulse and Urea. For example, CRP and TRAIL are measured; CRP andTRAIL and SAA are measured; CRP and TRAIL and Mac-2BP are measured; CRPand TRAIL and PCT and are measured; CRP and TRAIL and SAA and Mac-2BPare measured; PCT and TRAIL are measured; or SAA and TRAIL are measured.In a another aspect the method distinguishes between a mixed infectedsubject and a virally infected subject by measuring wherein one or moreDETERMINANTS selected from TRAIL, IP10, IL1RA, CHI3L1, CMPK2 and MCP-2are measured and optionally one or more DETERMINANTS selected from thegroup consisting of CRP, SAA, ANC, ATP6V0B, CES1, CORO1A, HERC5, IFITM1,LIPT1, LOC26010, LRDD, Lym (%), MCP-2, MX1, Neu (%), OAS2, PARP9, RSAD2,SART3, WBC, PCT, IL-8, IL6 and TREM-1.

In another aspect the method distinguishes between a bacterial or mixedinfected subject and a virally infected subject by measuring wherein oneor more DETERMINANTS selected from TRAIL, IL1RA, IP10, ARG1, CD337,CD73, CD84, CHI3L1, CHP, CMPK2, CORO1C, EIF2AK2, Eotaxin, GPR162,HLA-A/B/C, ISG15, ITGAM, Mac-2BP, NRG1, RAP1B, RPL22L1, SSEA1, RSAD2,RTN3, SELI, VEGFR2, CD62L and VEGFR2 are measured and optionally one ormore DETERMINANTS selected from the group consisting of CRP, SAA, PCT,IL6, IL8, ADIPOR1, ANC, Age, B2M, Bili total, CD15, Cr, EIF4B, IFIT1,IFIT3, IFITM1, IL7R, K (potassium), KIAA0082, LOC26010, Lym (%), MBOAT2,MCP-2, MX1, Na, Neu (%), OAS2, PARP9, PTEN, Pulse, Urea, WBC, ZBP1,mIgG1 and TREM-1.

In another aspect the method distinguishes between a subject with aninfectious disease and a subject with a non-infectious disease or ahealthy subject by measuring one or more DETERMINANTS selected fromIP10, IL1RA, TRAIL, BCA-1, CCL19-MIP3b, CES1 and CMPK2. Optionally, oneor more DETERMINANTS selected from CRP, SAA, PCT, IL6, IL8, ARPC2,ATP6V0B, Cr, Eos (%), HERC5, IFI6, IFIT3, KIAA0082, LIPT1, LOC26010,LRDD, MBOAT2, MX1, Maximal temperature, OAS2, PARP9, Pulse, QARS, RAB13,RPL34, RSAD2, SART3, RIM22, UBE2N, XAF1, IL11, I-TAC and TNFR1 aremeasured.

Any of the above described methods can be used to further select atreatment regimen for the subject. For example, if a subject identifiedas having a viral infection the subject is selected to receive ananti-viral treatment regimen. When a subject is identified as having anon-viral disease the subject is selected not to receive an anti-viraltreatment regimen. When a subject is identified as having a bacterial ora mixed infection the subject is selected to receive an antibiotictreatment regimen. When a subject identified as having a viralinfection, a non-infectious disease or healthy the subject is notselected to receive an antibiotic treatment regimen.

In a further aspect the invention provides for monitoring theeffectiveness of treatment for an infection by detecting the level ofone or more polypeptide-DETERMINANTS selected from the group consistingof TRAIL, IL1RA, IP10, B2M, Mac-2BP, BCA-1, CHI3L1, Eotaxin, MCP,Mac-2BP, TRAIL, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15,RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A, CD335, CD337, CD45,CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1,RAP1B, SELI, SPINT2, SSEA1, IL1, IL1a, I-TAC and TNFR1 in a first samplefrom the subject at a first period of time; detecting the level of oneor more polypeptide-DETERMINANTS selected from the group consisting ofTRAIL, IL1RA, IP10, B2M, Mac-2BP, BCA-1, CHI3L1, EotaxinMCP, Mac-2BP,TRAIL, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1, RTN3,CD112, CD134, CD182, CD231, CD235A, CD335, CD337, CD45, CD49D,CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B,SELI, SPINT2, SSEA1, IL11, IL1a, I-TAC and TNFR1 in a second sample fromthe subject at a second period of time; and comparing the level of theone or more polypeptide detected in the first sample to the leveldetected the second sample, or to a reference value, The effectivenessof treatment is monitored by a change in the level of one or morepolypeptides. Optionally, the method further includes detecting one ormore polypeptide-DETERMINANTS selected from CRP, SAA, TREM-1, PCT, IL-8and IL6 in the first and second samples.

The subject has previously been treated for the infection. Alternativelythe subject has not been previously treated for the infection. In someaspects the first sample is taken from the subject prior to beingtreated for the infection and the second sample is taken from thesubject after being treated for the infection. In some aspects, thesecond sample is taken from the subject after recurrence of theinfection or prior to recurrence of the infection.

The sample is for example, whole blood or a fraction thereof. A bloodfraction sample contains cells that include lymphocytes, monocytes andgranulocytes. The expression level of the polypeptide is determined byelectrophoretically, or immunochemically. The immunochemical detectionis for example, by flow cytometry, radioimmunoassay, immunofluorescenceassay or by an enzyme-linked immunosorbent assay.

A clinically significant alteration in the level of the one or morepolypeptides in the sample indicates an infection in the subject. Insome aspects the level of the one or more DETERMINANTS is compared to areference value, such as an index value. In some aspects the referencevalue or index value are determined after performing age dependentnormalization or stratification. In any of the above methods theDETERMINANTS are preferably selected such that their MCC is >=0.4 or theAUC is >=0.7. In other aspects DETERMINANTS are preferably selected suchthat their Wilcoxon rank sum p-values are less than 10⁻⁶ or less than10⁻⁴ or less than 10⁻³.

In any of the above methods the concentration of TRAIL is measuredwithin about 24 hours after sample is obtained or is measured in asample that was stored at 120 C or lower, wherein the storage beginsless than 24 hours after the sample is obtained.

The infection further includes an infection reference expressionprofile, having a pattern of levels of two or more polypeptides selectedfrom the group consisting of TRAIL, IL1RA, IP10, B2M, BCA-1, CHI3L1,Eotaxin, MCP, Mac-2BP, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2,ISG15, RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A, CD335, CD337,CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM,NRG1, RAP1B, SELI, SPINT2, SSEA1, IL11, IL1a, I-TAC and TNFR1, andoptionally further having a pattern of levels of one or morepolypeptides selected from the group consisting of CRP, SAA, TREM-1,PCT, IL-8 and IL6. Also include in the invention is a machine readablemedia containing one or more infection reference expression profilesaccording to the invention.

In another aspect the invention includes a kit having a plurality ofpolypeptide detection reagents that detect the correspondingpolypeptides including TRAIL, IL1RA, IP10, B2M, BCA-1, CHI3L1, Eotaxin,IL1a, MCP, Mac-2BP, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15,RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A, CD335, CD337, CD45,CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1,RAP1B, SELI, SPINT2, SSEA1, IL11, I-TAC and TNFR1, and optionallyfurther plurality of polypeptide detection reagents that detect thecorresponding polypeptide including CRP, SAA, TREM-1, PCT, IL-8 and IL6.The detection reagent is comprises one or more antibodies or fragmentsthereof.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention pertains. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice of the present invention, suitable methods and materials aredescribed below.

All publications, patent applications, patents, and other referencesmentioned herein are expressly incorporated by reference in theirentirety. In cases of conflict, the present specification, includingdefinitions, will control. In addition, the materials, methods, andexamples described herein are illustrative only and are not intended tobe limiting.

Other features and advantages of the invention will be apparent from andencompassed by the following detailed description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: Clinical study workflow.

FIG. 2: Characterization of the 575 patients enrolled in the clinicalstudy.

FIG. 3: Summary of patient cohorts.

FIGS. 4A and 4B: Age distribution of the entire study population (A)(N=575) and pediatric patients (B) (N=350).

FIGS. 5A and 5B: Distribution of isolated pathogens by pathogenicsubgroups (A) and by strains (B) (stains isolated from >1% of patientsare presented).

FIG. 6: Distribution of involved physiologic systems in infectiousdisease patients. (N=484).

FIGS. 7A and 7B: Distribution of major clinical syndromes (A) andspecific clinical syndromes (B) of the patients enrolled in the clinicalstudy (all enrolled patients, N=575).

FIG. 8: Distribution of maximal body temperatures (all enrolledpatients, N=575).

FIG. 9: Distribution of time from initiation of symptoms (all enrolledpatients, N=575).

FIGS. 10A and 10B: Distribution of comorbidities of the patientpopulation (A) and distribution of chronic medications (B) of thepatients enrolled in the clinical study (all chronically ill patients,N=170).

FIG. 11: Distribution of recruitment sites (all enrolled patients,N=575).

FIGS. 12A, 12B and 12C: Calibration curves for TRAIL (A), Mac-2BP (B)and SAA (C).

FIGS. 13A, 13B and 13C: Intra-assay variability for TRAIL (A), Mac-2-BP(B) and SAA (C).

FIGS. 14A, 14B and 14C: Inter-assay variability for TRAIL (A), Mac-2-BP(B) and SAA (C).

FIGS. 15A, 15B and 15C: Measurements of plasma vs. serum concentrationsof TRAIL (A), Mac-2-BP (B) and SAA (C).

FIGS. 16A, 16B and 16C: The analytes decay rates at 25° C. for TRAIL(A), Mac-2-BP (B) and SAA (C).

FIG. 17: Correlation of TRAIL levels measured using ELISA and Luminex.

FIGS. 18A, 18B, 18C, 18D, 18E, 18F, 18G and 18H: Polypeptides with animmunological role do not necessarily show a differential response.

FIG. 19: In-vitro differentially expressed polypeptides do notnecessarily show in-vivo differential expression.

FIGS. 20A, 20B, 20C, 20D, 20E, 20F, 20G, 20H, 20I, 20J, 20K, 20L, 20M,20N, 20O, 20P, 20Q, 20R, 20S and 20T: Examples of DETERMINANTS thatdifferentiate between bacterial versus viral infected subjects.

FIGS. 21A-1, 21A-2, 21A-3, 21A-4, 21A-5, 21A-6, 21A-7, 21A-8, 21A-9,21A-10, 21A-11, 21A-12, 21B-1, 21B-2, 21B-3, 21B-4, 21C-1, 21C-2 and21C-3: Examples of DETERMINANTS that differentiate between mixed versusviral infected subjects (A), infectious versus non-infectious subjects(B) and infectious versus healthy subjects (C).

FIG. 22: Colonization of non-infectious and healthy subjects.

FIGS. 23A and 23B: Examples of scatter graphs showing the diagnosis ofbacterial (‘+’ marks) versus viral (‘0’ marks) infected patients using acombination of two statistically significant DETERMINANTS. Patientclassification was performed using a linear SVM trained on 90% of thedata, where white and gray regions indicate the space of DETERMINANTcombinations that were classified as viral and bacterial respectively.Each plot corresponds to a different combination of two DETERMINANTS.

FIG. 24: Examples of scatter graphs showing the diagnosis of Mixed (‘+’marks) versus viral (‘0’ marks) infected patients using a combination oftwo statistically significant DETERMINANTS.

FIG. 25: The TCM-signature accuracy in diagnosing bacterial vs. viralinfections in patients whose diagnosis was clear. The analysis wasperformed using the ‘Clear (bacterial, viral)’ cohort; N=170.

FIG. 26: The TCM-signature accuracy in diagnosing bacterial vs. viralinfections in patients whose diagnosis was determined by a consensus ofexperts. The analysis was performed using the ‘Consensus (bacterial,viral)’ cohort.

FIG. 27: the TCM-signature accuracy in diagnosing bacterial vs. viralpatients in patients whose diagnosis was determined by majority of anexpert panel. The analysis was performed using the ‘Majority (bacterial,viral)’ cohort.

FIG. 28: the TCM-signature accuracy in distinguishing mixedco-infections from pure viral infections in patients whose diagnosis wasdetermined by majority of an expert panel. The analysis was performedusing the ‘Majority (viral, mixed)’ cohort.

FIGS. 29A and 29B: The TCM-signature accuracy in diagnosing bacterialvs. viral patients in the ‘Consensus (bacterial, viral)’ cohort and the‘Majority (bacterial, viral)’ cohort before and after inclusion ofpatients who were initially excluded from the study.

FIG. 30: Accuracy of the TCM-signature as a function of time fromsymptom onset. Error bars represent 95% CI.

FIG. 31: Accuracy of the TCM-signature as a function of maximal fevermeasured. Error bars represent 95% CI.

FIG. 32: DETERMINANT levels in different infections as a function ofAge.

FIGS. 33A and 33B: Prevalence of select bacterial and viral strains inpatients with non-infectious (A) and infectious diseases (B) in the‘Majority (bacterial, viral, mixed, non-infectious)’ cohort.

FIG. 34: TCM signature performance in patients with (+) and without (−)colonization by select bacterial and viral strains. Error bars represent95% CI.

FIG. 35: Scatter plots (left panel), box plots (middle panel) and theapproximation of the log normal distributions (right panel) of thelevels of TRAIL in bacterial and viral patients. The analysis wasperformed using the ‘Consensus (bacterial, viral)’ cohort, N=434.

FIG. 36: ROC curve for the analyte TRAIL. The analysis was performedusing the ‘Consensus [bacterial, viral]’ cohort, N=343.

FIG. 37: The balance between the number of patients diagnosed and theaccuracy of the TRAIL assay.

FIGS. 38A-1, 38A-2, 38A-3 and 38B: Examples of DETERMINANTS whose mRNAlevels have been found to be differentially expressed in viral comparedto bacterial infections, but their polypeptide levels in bacterialversus viral infected patients show no significant differentialresponse. (A) The protein levels of IFI44, IFI44L and IFI27 in bacterial(diamonds) and viral (squares) infections. (B) The mRNA expressionlevels of the IFI44, IFI44L, and IFI27 genes in bacterial (diamonds) andviral (squares) infections. Median value is indicated with a solid line.

FIG. 39: TCM-signature sensitivity and specificity increase as thecutoffs used for filtering out patients with marginal responses becomemore stringent. The analysis was performed using the ‘Consensus(bacterial, viral)’ cohort. Every point corresponds to the sensitivityand specificity attained at the cutoff in which the two measures werekept equal.

FIG. 40: TCM-signature sensitivity and specificity increase as thecutoffs used for filtering out patients with marginal responses becomemore stringent. The analysis was performed using the ‘Majority(bacterial, viral)’ cohort. Every point corresponds to the sensitivityand specificity attained at the cutoff in which the two measures werekept equal.

FIGS. 41A, 41B and 41C: The levels of TRAIL increase during the acutephase of a viral infection and then gradually decrease to baselinelevels (A, B). In patients with an acute bacterial infection its levelsdecrease and then increase back to baseline levels during convalescence(C).

FIG. 42: Comparison of the genetic sequence of TRAIL across organisms.

DETAILED DESCRIPTION OF THE INVENTION

The present invention, in some embodiments thereof, relates to theidentification of signatures and determinants associated with bacterial,viral and mixed (i.e., bacterial and viral co-infections) infections.More specifically we discovered that certain polypeptide-DETERMINANTSare differentially expressed in a statistically significant manner insubjects with bacteria, viral or mixed (i.e., bacterial and viralco-infections) as well as non-infectious disease and healthy subjects.These polypeptide-DETERMINANTS include TRAIL, IL1RA, IP10, Mac-2BP, B2M,BCA-1, CHI3L1, Eotaxin, IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C,EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A,CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162,HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1, IgG non-specificbound molecules, IL1, I-TAC, TNFR1, IFITM3, IFIT3, EIF4B, IFIT1,LOC26010, MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A, IFITM1, IL7,CRP, SAA, TREM-1, PCT, IL-8, TREM-1, IL6, ARG1, ARPC2, ATP6V0B, BCA-1,BRI3BP, CCL19-MIP3b, CES1, CORO1A, HERC5, IFI6, IFIT3, KIAA0082, LIPT1,LRDD, MCP-2, PARP9, PTEN, QARS, RAB13, RPL34, SART3, TRIM22, UBE2N, XAF1and ZBP1.

In some embodiments the polypeptide-DETERMINANTS aresoluble-polypeptides that include B2M, BCA-1, CHI3L1, Eotaxin, IL1a,IP10, MCP, Mac-2BP, TRAIL, CD62L, VEGFR2, IL11, IL1RA, I-TAC and TNFR1.

In other embodiments the polypeptide-DETERMINANTS areintracellular-polypeptides that include CHP, CMPK2, CORO1C, EIF2AK2,ISG15, RPL22L1 and RTN3.

In other embodiments the polypeptide-DETERMINANTS are membranepolypeptides that include CD112, CD134, CD182, CD231, CD235A, CD335,CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C,ITGAM, NRG1, RAP1B, SELI, SPINT2 and SSEA1.

In other embodiments the polypeptide-DETERMINANTS further includepolypeptides selected from the group consisting of EIF4B, IFIT1, IFIT3,LOC26010, MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A, IFITM1, IFITM3,IL7R, CRP, SAA, sTREM, PCT, IL-8 and IL6.

In other embodiments the DETERMINANTS further includeclinical-DETERMINANTS selected from the group consisting of: ANC, ALC,Neu (%), Lym (%), Mono (%), Maximal temperature, Time from symptoms,Age, Creatinine (Cr), Potassium (K), Pulse and Urea.

In some embodiments, the DETERMINANTS further comprise measurements ofone or more polypeptides or clinical-DETERMINANTS selected from thegroup consisting of: ARG1, ARPC2, ATP6V0B, BILI (BILIRUBIN), BRI3BP,CCL19-MIP3B, CES1, CORO1A, EOS (%), HERC5, IFI6, IFIT3, KIAA0082, LIPT1,LRDD, MCP-2, NA (Sodium), PARP9, PTEN, QARS, RAB13, RPL34, SART3,TRIM22, UBE2N, WBC (Whole Blood Count), XAF1 and ZBP1.

Different infectious agents have unique molecular patterns that can beidentified and targeted by the immune system. Pathogen-associatedmolecular patterns (PAMPs) are an example of such molecules that areassociated with different groups of pathogens and may be recognized bycells of the innate immune system using Toll-like receptors (TLRs) andother pattern recognition receptors (e.g. NOD proteins) (Akira, S. andS. Uematsu, et al 2006; Murphy, K. and P. Travers, et al 2007). Thesepatterns may vary considerably between different classes of pathogensand thus elicit different immune responses. For example, TLR-4 canrecognize lipopolysaccharide, a constituent of gram negative bacteria,as well as lipoteichoic acids, constituent of gram positive bacteria,hence promoting an anti-microbial response of the immune system (Akira,S. and S. Uematsu, et al 2006; Murphy, K. and P. Travers, et al 2007).TLR-3 can recognize single stranded RNA (often indicative of a viralinfection) and thus prompt the appropriate anti-viral response (Akira,S. and S. Uematsu, et al 2006; Murphy, K. and P. Travers, et al 2007).By distinguishing between different classes of pathogens (e.g bacterialversus viral) the immune system can mount the appropriate defense.

In the past few decades, several host markers have been identified thatcan be used for differential diagnosis of infection source in variousindications. One example is Procalcitonin (PCT), a precursor of thehormone calcitonin produced by the C-cells of the thyroid gland. PCTlevels in the blood stream of healthy individuals is hardly detectable(in the pg/ml range) but it might increase dramatically, as a result ofa severe infection with levels rising up to 100 ng/ml.

PCT is heavily used to diagnose patients with systemic infection,sepsis, with sensitivity of 76% and specificity of 70% (Jones, A. E. andJ. F. Fiechtl, et al 2007). However, studies that tested the diagnosticvalue of PCT in other non-systemic infection such as pneumonia or upperrespiratory tract infections found it to be limited (Brunkhorst, F. M.and B. Al-Nawas, et al 2002; Tang M. P. and Eslick G D 2007), especiallywhen used in isolation.

Another widely used marker is the acute phase protein, C-reactiveprotein (CRP). CRP levels in the blood often rise in response toinflammation. Therefore, when used as an adjunct biomarker in the rightclinical context, CRP may prove useful for improving detection accuracyof infections (Povoa P. 2002). However, in some indications such assepsis its specificity and sensitivity were found to be considerablylower than PCT (Hatherill, M. and S. M. Tibby, et al 1999).Additionally, its clinical utility as a stand-alone marker for Abxprescription decision making has been criticized (Brian Clyne andJonathan S Olshaker 1999).

One reason for CRP's limited accuracy in the context of infectiousdisease stems from the fact that CRP may rise in indications other thanbacterial infection. For example some viral infections includingadenoviruses (Appenzeller C et al. 2002; A. Putto, O. Meurman, and O.Ruuskanen 1986) are known to cause a significant increase in the levelsof CRP that mimics a bacterial response, thus limiting CRP's accuracy asa single marker for differentiating between viral and bacterialinfections. CRP may also rise in non-infectious disease such as trauma.Other proposed markers for detection of different sources of infectionand sepsis include CD64 (Rudensky, B. and G. Sirota, et al 2008), andHNL (Fjaertoft, G. and T. Foucard, et al. 2005). The reliability andevidence supporting the usage of these markers for the purpose ofdiagnostics of viral versus bacterial infections in a broad setting arelimited.

The present invention, in some embodiments thereof, seeks to overcomethe above mentioned diagnostic challenges by: (i) enabling accuratedifferentiation between a broad range of bacterial versus viralinfections; (ii) enabling rapid diagnostics (within minutes); (iii)avoiding the “false positive” identification of non-pathogenic bacteriathat are part of the body's natural flora, (iv) allowing accuratedifferentiation between mixed and pure viral infections and (v) allowingdiagnosis in cases where the pathogen is inaccessible.

To this end the inventors sought to identify and test a novel set ofbiomarkers whose levels are differentially expressed in viral, bacterialand mixed infected patients, and in patients with a non-infectiousdisease and to use the combined measurements of these biomarkers coupledwith pattern recognition algorithms to accurately identify the source ofinfection with the aim of assisting physicians to accurately prescribethe correct treatment.

To facilitate a solution that is generally applicable, the inventorsperformed a large clinical trial in which they enrolled a heterogeneouscohort of 655 patients including different ages, medical backgrounds,ethnicities, pathogen types, clinical syndromes and time from appearanceof symptoms, fever, co-morbidities (see FIGS. 4-10). The inventors thenmeasured the levels of over 570 different polypeptides usingquantitative assays, and were able to screen a small subset ofpolypeptides that was robustly differentially expressed in differenttypes of infections. They used the combined signature of these selectedpolypeptides to develop and test various aspects of the presentsolution.

To address the challenge of rapid diagnosis, some aspects of theinvention focus on biomarkers that can be rapidly measured, such asproteins, rather than biomarkers whose measurement may require hours todays, such as nucleic-acid based biomarkers. Note that high-throughputquantitative measurements of nucleic-acids for the purpose of biomarkerdiscovery have become feasible in recent years using technologies suchas microarrays and deep sequencing. However, performing suchquantitative high-throughput measurements on the proteome level remainsa challenge. Thus, some aspects of the present invention focus on theproteome level.

To address the clinical challenge of mixed infection diagnosis andtreatment, some aspects of the present invention include a method fordifferentiating between mixed infections (which require Abx treatmentdespite the presence of a virus) and pure viral infections (which do notrequire Abx treatment).

Some aspects of the present invention also address the challenge of“false-positive” diagnostics due to non-pathogenic strains of bacteriathat are part of the body's natural flora. This is achieved by measuringbiomarkers derived from the host rather than the pathogen.

Another aspect of the present invention enables the diagnosis ofdifferent infections, which is invariant to the presence or absence ofcolonizers (e.g. bacteria and viruses that are part of the naturalflora). This addresses one of the major challenges in infectious diseasediagnostics today: “false-positives” due to colonizers.

Importantly, some aspects of the current invention do not require directaccess to the pathogen, because the immune system circulates in theentire body, thereby facilitating diagnosis in cases in which thepathogen is inaccessible.

Another aspect of the present invention is the fraction in which thebiomarkers are measured, which affects the ease by which the assay canbe performed in the clinical settings, and especially the point-of-care.For example, it is easier to measure proteins in the serum or plasmafraction compared to nucleic acids or intra-cellular proteins in theleukocytes fraction (the latter requires an additional experimental stepin which leukocytes are isolated from the whole blood sample, washed andlysed). Accordingly, some aspects of the present invention also describeserum and plasma based protein signatures that are easily measurableusing various immunoassays available in clinical settings.

Other aspects of the invention provide methods for identifying subjectswho have an infection by the detection of DETERMINANTS associated withan infection, including those subjects who are asymptomatic for theinfection. These signatures and DETERMINANTS are also useful formonitoring subjects undergoing treatments and therapies for infection,and for selecting or modifying diagnostics, therapies and treatmentsthat would be efficacious in subjects having an infection.

Exemplary Polypeptide-DETERMINANT Measured in the Present Invention

The polypeptide-DETERMINANT names presented herein are given by way ofexample. Many alternative names, aliases, modifications, isoforms andvariations will be apparent to those skilled in the art. Accordingly, itis intended to embrace all the alternative protein names, aliases,modifications isoforms and variations.B2M: additional alias of B2M include without limitationbeta-2-microglobulin and CDABP0092. B2M is a component of MHC class Imolecules, which are present on all nucleated cells. The protein encodedby this gene also encodes an isoform present in the serum.

The protein has a predominantly beta-pleated sheet structure that canform amyloid fibrils in some pathological conditions.

BCA1: BCA1 is a B lymphocyte chemoattractant, independently cloned andnamed Angie, is a CXC chemokine strongly expressed in the follicles ofthe spleen, lymph nodes, and Peyer's patches. It preferentially promotesthe migration of B lymphocytes (compared to T cells and macrophages),apparently by stimulating calcium influx into, and chemotaxis of, cellsexpressing Burkitt's lymphoma receptor 1 (BLR-1). It may thereforefunction in the homing of B lymphocytes to follicles (provided byRefSeq).

CHI3L1: chitinase 3-like 1 (cartilage glycoprotein-39); additionalaliases of CHI3L1 include without limitation ASRT7, CGP-39, GP-39, GP39,HC-gp39, HCGP-3P, YKL-40, YKL40, YYL-40 and hCGP-39. Chitinases catalyzethe hydrolysis of chitin, which is an abundant glycopolymer found ininsect exoskeletons and fungal cell walls. The glycoside hydrolase 18family of chitinases includes eight human family members. This geneencodes a glycoprotein member of the glycosyl hydrolase 18 family thatlacks chitinase activity can be secreted by activated macrophages,chondrocytes, neutrophils and synovial cells. CHI3L1 inhibitsoxidant-induced lung injury, augments adaptive Th2 immunity, regulatesapoptosis, stimulates alternative macrophage activation, and contributesto fibrosis and wound healing.

Eotaxin: This gene is one of several Cys-Cys (CC) cytokine genesclustered on the q-arm of chromosome 17. Cytokines are a family ofsecreted proteins involved in immunoregulatory and inflammatoryprocesses. The CC cytokines are proteins characterized by two adjacentcysteines. The cytokine encoded by this gene displays chemotacticactivity for eosinophils, but not mononuclear cells or neutrophils. Thiseosinophil specific chemokine assumed to be involved in eosinophilicinflammatory diseases such as atopic dermatitis, allergic rhinitis,asthma and parasitic infections (provided by RefSeq). In response to thepresence of allergens, this protein directly promotes the accumulationof eosinophils, a prominent feature of allergic inflammatory reactions.

IL1A: The protein encoded by this gene is a member of the interleukin 1cytokine family. This cytokine is a pleiotropic cytokine involved invarious immune responses, inflammatory processes, and hematopoiesis.This cytokine can be produced by monocytes and macrophages as aproprotein, which is proteolytically processed and released in responseto cell injury, and thus induces apoptosis. This gene and eight otherinterleukin 1 family genes form a cytokine gene cluster on chromosome 2.IL-1 proteins are involved in the inflammatory response, beingidentified as endogenous pyrogens, and are reported to stimulate therelease of prostaglandin and collagenase from synovial cells.

MCP: The protein encoded by this gene is a type I membrane protein andis a regulatory part of the complement system. The encoded protein hascofactor activity for inactivation of complement components C3b and C4bby serum factor I, which protects the host cell from damage bycomplement.

Edmonston strain of measles virus, human herpesvirus-6, and type IV piliof pathogenic Neisseria. The protein encoded by this gene may beinvolved in the fusion of the spermatozoa with the oocyte duringfertilization. Mutations at this locus have been associated withsusceptibility to hemolytic uremic syndrome. Alternatively splicedtranscript variants encoding different isoforms have been described(provided by RefSeq).

MAC-2-BP: Additional aliases of MAC-2-BP include without limitationLGALS3BP, 90K, serum protein 90K, BTBD17B, M2BP and lectin,galactoside-binding, soluble, 3 binding protein. The galectins are afamily of beta-galactoside-binding proteins implicated in modulatingcell-cell and cell-matrix interactions. The levels of MAC-2-BP werefound to be elevated in the serum of cancer patients. It appears to beimplicated in immune response associated with natural killer (NK) andlymphokine-activated killer (LAK) cell cytotoxicity. The native proteincan bind specifically to a human macrophage-associated lectin known asMac-2 as well as galectin 1.

CD62L: This gene encodes a cell surface adhesion molecule that belongsto a family of adhesion/homing receptors. The encoded protein contains aC-type lectin-like domain, a calcium-binding epidermal growthfactor-like domain, and two short complement-like repeats. The geneproduct is required for binding and subsequent rolling of leucocytes onendothelial cells, facilitating their migration into secondary lymphoidorgans and inflammation sites. Single-nucleotide polymorphisms in thisgene have been associated with various diseases including immunoglobulinA nephropathy. Alternatively spliced transcript variants have been foundfor this gene (provided by RefSeq). The protein encoded by this gene hasa soluble form denoted sCD62L.

VEGFR2: Vascular endothelial growth factor (VEGF) is a major growthfactor for endothelial cells. This gene encodes one of the two receptorsof the VEGF. This receptor, known as kinase insert domain receptor, is atype III receptor tyrosine kinase. It functions as the main mediator ofVEGF-induced endothelial proliferation, survival, migration, tubularmorphogenesis and sprouting. The signaling and trafficking of thisreceptor are regulated by multiple factors, including Rab GTPase, P2Ypurine nucleotide receptor, integrin alphaVbeta3, T-cell proteintyrosine phosphatase, etc. Mutations of this gene are implicated ininfantile capillary hemangiomas (provided by RefSeq). The proteinencoded by this gene has a soluble form denoted sVEGFR2.

TRAIL: The protein encoded by this gene is a cytokine that belongs tothe tumor necrosis factor (TNF) ligand family. Additional names of thegene include without limitations APO2L, TNF-related apoptosis-inducingligand, TNFSF10 and CD253. TRAIL exists in a membrane bound form and asoluble form, both of which can induce apoptosis in different cells,such as transformed tumor cells. This protein binds to several membersof the TNF receptor superfamily such as TNFRSF10A/TRAILR1,NFRSF10B/TRAILR2, NFRSF10C/TRAILR3, TNFRSF10D/TRAILR4, and possibly alsoto NFRSF11B/OPG. The activity of this protein may be modulated bybinding to the decoy receptors such as NFRSF10C/TRAILR3,TNFRSF10D/TRAILR4, and NFRSF11B/OPG that cannot induce apoptosis. Thebinding of this protein to its receptors has been shown to trigger theactivation of MAPK8/JNK, caspase 8, and caspase 3. Alternatively splicedtranscript variants encoding different isoforms have been found for thisgene. TRAIL can be proteolytically cleaved from the cell surface toproduce a soluble form that has a homotrimeric structure.

CHP: This gene encodes a phosphoprotein that binds to the Na+/H+exchanger NHE1. This protein serves as an essential cofactor whichsupports the physiological activity of NHE family members and may play arole in the mitogenic regulation of NHE1. The protein shares similaritywith calcineurin B and calmodulin and it is also known to be anendogenous inhibitor of calcineurin activity (provided by RefSeq).

CMPK2: This gene encodes a protein that may participate in dUTP and dCTPsynthesis in mitochondria. Is able to phosphorylate dUMP, dCMP, CMP, UMPand monophosphates of the pyrimidine nucleoside analogs ddC, dFdC, araC,BVDU and FdUrd with ATP as phosphate donor. Efficacy is highest for dUMPfollowed by dCMP; CMP and UMP are poor substrates. May be involved inmtDNA depletion caused by long term treatment with ddC or otherpyrimidine analogs.

CORO1C: This gene encodes a member of the WD repeat protein family. WDrepeats are minimally conserved regions of approximately 40 amino acidstypically bracketed by gly-his and trp-asp (GH-WD), which may facilitateformation of heterotrimeric or multiprotein complexes. Members of thisfamily are involved in a variety of cellular processes, including cellcycle progression, signal transduction, apoptosis, and gene regulation.

EIF2AK2: EIF2AK2 is a protein serine/threonine kinase that acquiresenzymatic activity following autophosphorylation, a process mediated bydouble-stranded RNA (dsRNA). Additional aliases include withoutlimitation: PKR, PRKR, EIF2AK1, protein kinase, interferon-inducibledouble stranded RNA dependent, p68 kinase, etc. Activation of EIF2AK2allows the kinase to phosphorylate its natural substrate, the alphasubunit of eukaryotic protein synthesis initiation factor-2 (EIF2-alpha;MIM 603907), leading to the inhibition of protein synthesis.

ISG15: ISG15 ubiquitin-like modifier; additional aliases of ISG15include without limitation G1P2, IFI15, IP17, UCRP and hUCRP. Thisubiquitin-like protein is conjugated to intracellular target proteinsafter IFN-alpha or IFN-beta stimulation. Its enzymatic pathway ispartially distinct from that of ubiquitin, differing in substratespecificity and interaction with ligating enzymes. ISG15 conjugationpathway uses a dedicated E1 enzyme, but seems to converge with the Ubconjugation pathway at the level of a specific E2 enzyme. Targetsinclude STAT1, SERPINA3G/SPI2A, JAK1, MAPK3/ERK1, PLCG1, EIF2AK2/PKR,MX1/MxA, and RIG-1. Shows specific chemotactic activity towardsneutrophils and activates them to induce release of eosinophilchemotactic factors. May serve as a trans-acting binding factordirecting the association of ligated target proteins to intermediatefilaments. May also be involved in autocrine, paracrine and endocrinemechanisms, as in cell-to-cell signaling, possibly partly by inducingIFN-gamma secretion by monocytes and macrophages.

RTN3: May be involved in membrane trafficking in the early secretorypathway. Inhibits BACE1 activity and amyloid precursor proteinprocessing. May induce caspase-8 cascade and apoptosis. May favor BCL2translocation to the mitochondria upon endoplasmic reticulum stress. Incase of enteroviruses infection, RTN3 may be involved in the viralreplication or pathogenesis.

CD112: This gene encodes a single-pass type I membrane glycoprotein withtwo Ig-like C2-type domains and an Ig-like V-type domain. This proteinis one of the plasma membrane components of adherens junctions. It alsoserves as an entry for certain mutant strains of herpes simplex virusand pseudorabies virus, and it is involved in cell to cell spreading ofthese viruses.

Variations in this gene have been associated with differences in theseverity of multiple sclerosis.

Alternate transcriptional splice variants, encoding different isoforms,have been characterized. (provided by RefSeq).

CD134: The protein encoded by this gene is a member of the TNF-receptorsuperfamily. This receptor has been shown to activate NF-kappaB throughits interaction with adaptor proteins TRAF2 and TRAF5. Knockout studiesin mice suggested that this receptor promotes the expression ofapoptosis inhibitors BCL2 and BCL21L1/BCL2-XL, and thus suppressesapoptosis. The knockout studies also suggested the roles of thisreceptor in CD4+ T cell response, as well as in T cell-dependent B cellproliferation and differentiation (provided by RefSeq).

CD182: The protein encoded by this gene is a member of theG-protein-coupled receptor family. This protein is a receptor forinterleukin 8 (IL8). It binds to IL8 with high affinity, and transducesthe signal through a G-protein activated second messenger system. Thisreceptor also binds to chemokine (C—X—C motif) ligand 1 (CXCL1/MGSA), aprotein with melanoma growth stimulating activity, and has been shown tobe a major component required for serum-dependent melanoma cell growth.This receptor mediates neutrophil migration to sites of inflammation.The angiogenic effects of IL8 in intestinal microvascular endothelialcells are found to be mediated by this receptor. Knockout studies inmice suggested that this receptor controls the positioning ofoligodendrocyte precursors in developing spinal cord by arresting theirmigration. This gene, IL8RA, a gene encoding another high affinity IL8receptor, as well as ILSRBP, a pseudogene of IL8RB, form a gene clusterin a region mapped to chromosome 2q33-q36. Alternatively splicedvariants, encoding the same protein, have been identified (provided byRefSeq).

CD231: The protein encoded by this gene is a member of the transmembrane4 superfamily, also known as the tetraspanin family. Most of thesemembers are cell-surface proteins that are characterized by the presenceof four hydrophobic domains. The proteins mediate signal transductionevents that play a role in the regulation of cell development,activation, growth and motility. This encoded protein is a cell surfaceglycoprotein and may have a role in the control of neurite outgrowth. Itis known to complex with integrins. This gene is associated withX-linked mental retardation and neuropsychiatric diseases such asHuntington's chorea, fragile X syndrome and myotonic dystrophy (providedby RefSeq).

CD235a: CD235a is the major intrinsic membrane protein of theerythrocyte. The N-terminal glycosylated segment, which lies outside theerythrocyte membrane, has MN blood group receptors. Appears to beimportant for the function of SLC4A1 and is required for high activityof SLC4A1. May be involved in translocation of SLC4A1 to the plasmamembrane. Is a receptor for influenza virus. Is a receptor forPlasmodium falciparum erythrocyte-binding antigen 175 (EBA-175); bindingof EBA-175 is dependent on sialic acid residues of the O-linked glycans.Appears to be a receptor for Hepatitis A virus (HAV).

CD335: Cytotoxicity-activating receptor that may contribute to theincreased efficiency of activated natural killer (NK) cells to mediatetumor cell lysis.

CD337: The protein encoded by this gene is a natural cytotoxicityreceptor (NCR) that may aid NK cells in the lysis of tumor cells. Theencoded protein interacts with CD3-zeta (CD247), a T-cell receptor. Asingle nucleotide polymorphism in the 5′ untranslated region of thisgene has been associated with mild malaria susceptibility. Threetranscript variants encoding different isoforms have been found for thisgene.

CD45: The protein encoded by this gene is a member of the proteintyrosine phosphatase (PTP) family. PTPs are known to be signalingmolecules that regulate a variety of cellular processes including cellgrowth, differentiation, mitotic cycle, and oncogenic transformation.This PTP contains an extracellular domain, a single transmembranesegment and two tandem intracytoplasmic catalytic domains, and thusbelongs to receptor type PTP. This gene is specifically expressed inhematopoietic cells. This PTP has been shown to be an essentialregulator of T- and B-cell antigen receptor signaling. It functionsthrough either direct interaction with components of the antigenreceptor complexes, or by activating various Src family kinases requiredfor the antigen receptor signaling. This PTP also suppresses JAKkinases, and thus functions as a regulator of cytokine receptorsignaling. Several alternatively spliced transcripts variants of thisgene, which encode distinct isoforms, have been reported.

CD49d: The product of this gene belongs to the integrin alpha chainfamily of proteins. Integrins are heterodimeric integral membraneproteins composed of an alpha chain and a beta chain. This gene encodesan alpha 4 chain. Unlike other integrin alpha chains, alpha 4 neithercontains an I-domain, nor undergoes disulfide-linked cleavage. Alpha 4chain associates with either beta 1 chain or beta 7 chain (provided byRefSeq).

CD66a: This gene encodes a member of the carcinoembryonic antigen (CEA)gene family, which belongs to the immunoglobulin superfamily. Twosubgroups of the CEA family, the CEA cell adhesion molecules and thepregnancy-specific glycoproteins, are located within a 1.2 Mb cluster onthe long arm of chromosome 19. Eleven pseudogenes of the CEA celladhesion molecule subgroup are also found in the cluster. The encodedprotein was originally described in bile ducts of liver as biliaryglycoprotein. Subsequently, it was found to be a cell-cell adhesionmolecule detected on leukocytes, epithelia, and endothelia. The encodedprotein mediates cell adhesion via homophilic as well as heterophilicbinding to other proteins of the subgroup. Multiple cellular activitieshave been attributed to the encoded protein, including roles in thedifferentiation and arrangement of tissue three-dimensional structure,angiogenesis, apoptosis, tumor suppression, metastasis, and themodulation of innate and adaptive immune responses. Multiple transcriptvariants encoding different isoforms have been reported.

CD66c: Carcinoembryonic antigen (CEA; MIM 114890) is one of the mostwidely used tumor markers in serum immunoassay determinations ofcarcinoma. An apparent lack of absolute cancer specificity for CEAprobably results in part from the presence in normal and neoplastictissues of antigens that share antigenic determinants with the 180-kDform of CEA (Barnett et al., 1988 (PubMed 3220478)). For backgroundinformation on the CEA family of genes, see CEACAM 1 (MIM 109770)(supplied by OMIM).

CD66d: This gene encodes a member of the family of carcinoembryonicantigen-related cell adhesion molecules (CEACAMs), which are used byseveral bacterial pathogens to bind and invade host cells. The encodedtransmembrane protein directs phagocytosis of several bacterial speciesthat is dependent on the small GTPase Rac. It is thought to serve animportant role in controlling human-specific pathogens by the innateimmune system. Alternatively spliced transcript variants have beendescribed, but their biological validity has not been determined(provided by RefSeq).

CD66e: CD66e, a member of the CEACAM subfamily, serves as a surfaceglycoprotein that plays a role in cell adhesion and in intracellularsignaling. CD66e also serves a receptor for E. coli Dr adhesins.

CD84: CD84 plays a role as adhesion receptor functioning by homophilicinteractions and by clustering. Recruits SH2 domain-containing proteinsSH2D1A/SAP. Increases proliferative responses of activated T-cells andSH2D1A/SAP does not seem to be required for this process. Homophilicinteractions enhance interferon gamma/IFNG secretion in lymphocytes andinduce platelet stimulation via a SH2D1A/SAP-dependent pathway. CD84 mayalso serve as a marker for hematopoietic progenitor cells

EGFR: The protein encoded by this gene is a transmembrane glycoproteinthat is a member of the protein kinase superfamily. This protein is areceptor for members of the epidermal growth factor family. EGFR is acell surface protein that binds to epidermal growth factor. Binding ofthe protein to a ligand induces receptor dimerization and tyrosineautophosphorylation and leads to cell proliferation. Mutations in thisgene are associated with lung cancer. Multiple alternatively splicedtranscript variants that encode different protein isoforms have beenfound for this gene (provided by RefSeq).

GPR162: This gene was identified upon genomic analysis of a gene-denseregion at human chromosome 12p13. It appears to be mainly expressed inthe brain; however, its function is not known. Alternatively splicedtranscript variants encoding different isoforms have been identified(provided by RefSeq).

HLA-A: HLA-A belongs to the HLA class I heavy chain paralogues. Thisclass I molecule is a heterodimer consisting of a heavy chain and alight chain (beta-2 microglobulin). The heavy chain is anchored in themembrane. Class I molecules play a central role in the immune system bypresenting peptides derived from the endoplasmic reticulum lumen. Theyare expressed in nearly all cells. The heavy chain is approximately 45kDa and its gene contains 8 exons. Exon 1 encodes the leader peptide,exons 2 and 3 encode the alpha1 and alpha2 domains, which both bind thepeptide, exon 4 encodes the alpha3 domain, exon 5 encodes thetransmembrane region, and exons 6 and 7 encode the cytoplasmic tail.Polymorphisms within exon 2 and exon 3 are responsible for the peptidebinding specificity of each class one molecule.

Typing for these polymorphisms is routinely done for bone marrow andkidney transplantation. Hundreds of HLA-A alleles have been described(provided by RefSeq).

HLA-B: HLA-B belongs to the HLA class I heavy chain paralogues. Thisclass I molecule is a heterodimer consisting of a heavy chain and alight chain (beta-2 microglobulin).

The heavy chain is anchored in the membrane. Class I molecules play acentral role in the immune system by presenting peptides derived fromthe endoplasmic reticulum lumen. They are expressed in nearly all cells.The heavy chain is approximately 45 kDa and its gene contains 8 exons.Exon 1 encodes the leader peptide, exon 2 and 3 encode the alpha1 andalpha2 domains, which both bind the peptide, exon 4 encodes the alpha3domain, exon 5 encodes the transmembrane region and exons 6 and 7 encodethe cytoplasmic tail. Polymorphisms within exon 2 and exon 3 areresponsible for the peptide binding specificity of each class onemolecule.

Typing for these polymorphisms is routinely done for bone marrow andkidney transplantation. Hundreds of HLA-B alleles have been described(provided by RefSeq).

HLA-C: HLA-C belongs to the HLA class I heavy chain paralogues. Thisclass I molecule is a heterodimer consisting of a heavy chain and alight chain (beta-2 microglobulin).

The heavy chain is anchored in the membrane. Class I molecules play acentral role in the immune system by presenting peptides derived fromendoplasmic reticulum lumen. They are expressed in nearly all cells. Theheavy chain is approximately 45 kDa and its gene contains 8 exons. Exonone encodes the leader peptide, exons 2 and 3 encode the alpha1 andalpha2 domain, which both bind the peptide, exon 4 encodes the alpha3domain, exon 5 encodes the transmembrane region, and exons 6 and 7encode the cytoplasmic tail. Polymorphisms within exon 2 and exon 3 areresponsible for the peptide binding specificity of each class onemolecule.

Typing for these polymorphisms is routinely done for bone marrow andkidney transplantation. Over one hundred HLA-C alleles have beendescribed (provided by RefSeq).

ITGAM: This gene encodes the integrin alpha M chain. Integrins areheterodimeric integral membrane proteins composed of an alpha chain anda beta chain. This I-domain containing alpha integrin combines with thebeta 2 chain (ITGB2) to form a leukocyte-specific integrin referred toas macrophage receptor 1 (‘Mac-1’), or inactivated-C3b (iC3b) receptor 3(‘CR3’). The alpha M beta 2 integrin is important in the adherence ofneutrophils and monocytes to stimulated endothelium, and also in thephagocytosis of complement coated particles. Multiple transcriptvariants encoding different isoforms have been found for this gene(provided by RefSeq).

NRG1: The protein encoded by this gene was originally identified as a44-kD glycoprotein that interacts with the NEU/ERBB2 receptor tyrosinekinase to increase its phosphorylation on tyrosine residues. Thisprotein is a signaling protein that mediates cell-cell interactions andplays critical roles in the growth and development of multiple organsystems. It is known that an extraordinary variety of different isoformsare produced from this gene through alternative promoter usage andsplicing. These isoforms are tissue-specifically expressed and differsignificantly in their structure, and thereby these isoforms areclassified into types I, II, III, IV, V and VI. The gene dysregulationhas been linked to diseases such as cancer, schizophrenia and bipolardisorder (BPD)(provided by RefSeq).

RAP1B: GTP-binding protein that possesses intrinsic GTPase activity.Contributes to the polarizing activity of KRIT1 and CDH5 in theestablishment and maintenance of correct endothelial cell polarity andvascular lumen. Required for the localization of phosphorylated PRKCZ,PARD3 and TIAM1 to the cell junction.

SELI: This gene encodes a selenoprotein, which contains a selenocysteine(Sec) residue at its active site. The selenocysteine is encoded by theUGA codon that normally signals translation termination. The 3′ UTR ofselenoprotein genes have a common stem-loop structure, the sec insertionsequence (SECIS), that is necessary for the recognition of UGA as a Seccodon rather than as a stop signal (provided by RefSeq).

SPINT2: This gene encodes a transmembrane protein with two extracellularKunitz domains that inhibits a variety of serine proteases. The proteininhibits HGF activator which prevents the formation of active hepatocytegrowth factor. This gene is a putative tumor suppressor, and mutationsin this gene result in congenital sodium diarrhea. Multiple transcriptvariants encoding different isoforms have been found for this gene(provided by RefSeq).

EIF4B: Required for the binding of mRNA to ribosomes. Functions in closeassociation with EIF4-F and EIF4-A. It binds near the 5′-terminal cap ofmRNA in the presence of EIF-4F and ATP. It promotes the ATPase activityand the ATP-dependent RNA unwinding activity of both EIF4-A and EIF4-F.

IFIT1: Interferon-induced protein with tetratricopeptide repeats.

IFITM3/IFITM2: IFN-induced antiviral protein that mediates cellularinnate immunity to at least three major human pathogens, namelyinfluenza A HINI virus, West Nile virus (WNV), and dengue virus (WNV),by inhibiting the early step(s) of replication.

RSAD2: Radical S-adenosyl methionine domain containing 2; additionalaliases of RSAD2 include without limitation 2510004L01Rik, cig33, cig5and vig1. RSAD2 can impair virus budding by disrupting lipid rafts atthe plasma membrane, a feature which is essential for the buddingprocess of many viruses. Acts through binding with and inactivatingFPPS, an enzyme involved in synthesis of cholesterol, farnesylated andgeranylated proteins, ubiquinone dolichol and heme.

ADIPOR1: ADIPOR1 is a receptor for globular and full-length adiponectin(APMI), an essential hormone secreted by adipocytes that acts as anantidiabetic. It is probably involved in metabolic pathways thatregulate lipid metabolism such as fatty acid oxidation. It mediatesincreased AMPK, PPARA ligand activity, fatty acid oxidation and glucoseuptake by adiponectin. ADIPOR1 has some high-affinity receptors forglobular adiponectin and low-affinity receptors for full-lengthadiponectin.

CD15 (FUT4): The product of this gene transfers fucose toN-acetyllactosamine polysaccharides to generate fucosylated carbohydratestructures. It catalyzes the synthesis of the non-sialylated antigen,Lewis x (CD15).

CD73: The protein encoded by this gene is a plasma membrane protein thatcatalyzes the conversion of extracellular nucleotides tomembrane-permeable nucleosides. The encoded protein is used as adeterminant of lymphocyte differentiation. Defects in this gene can leadto the calcification of joints and arteries. Two transcript variantsencoding different isoforms have been found for this gene.

CD8A: The CD8 antigen is a cell surface glycoprotein found on mostcytotoxic T lymphocytes that mediates efficient cell-cell interactionswithin the immune system. The CD8 antigen acts as a corepressor with theT-cell receptor on the T lymphocyte to recognize antigens displayed byan antigen presenting cell (APC) in the context of class I MHCmolecules. The coreceptor functions as either a homodimer composed oftwo alpha chains, or as a heterodimer composed of one alpha and one betachain. Both alpha and beta chains share significant homology toimmunoglobulin variable light chains. This gene encodes the CD8 alphachain isoforms. Multiple transcript variants encoding different isoformshave been found for this gene (provided by RefSeq).

IFITM1: IFN-induced antiviral protein that mediate cellular innateimmunity to at least three major human pathogens, namely influenza AH1N1 virus, West Nile virus, and dengue virus by inhibiting the earlystep(s) of replication. Plays a key role in the antiproliferative actionof IFN-gamma either by inhibiting the ERK activation or by arrestingcell growth in G1 phase in a p53-dependent manner. Implicated in thecontrol of cell growth. Component of a multimeric complex involved inthe transduction of antiproliferative and homotypic adhesion signals.

IFITM3: IFN-induced antiviral protein that mediates cellular innateimmunity to at least three major human pathogens, namely influenza AH1N1 virus, West Nile virus (WNV), and dengue virus (WNV), by inhibitingthe early step(s) of replication.

IL7R: The protein encoded by this gene is a receptor for interleukine 7(IL7). The function of this receptor requires the interleukin 2receptor, gamma chain (IL2RG), which is a common gamma chain shared bythe receptors of various cytokines, including interleukin 2, 4, 7, 9,and 15. This protein has been shown to play a critical role in the V(D)Jrecombination during lymphocyte development. This protein is also foundto control the accessibility of the TCR gamma locus by STATS and histoneacetylation. Knockout studies in mice suggested that blocking apoptosisis an essential function of this protein during differentiation andactivation of T lymphocytes. The functional defects in this protein maybe associated with the pathogenesis of the severe combinedimmunodeficiency (SCID).

CRP: C-reactive protein; additional aliases of CRP include withoutlimitation RP11-419N10.4 and PTX1. The protein encoded by this genebelongs to the pentaxin family. It is involved in several host defenserelated functions based on its ability to recognize foreign pathogensand damaged cells of the host and to initiate their elimination byinteracting with humoral and cellular effector systems in the blood.Consequently, the level of this protein in plasma increases greatlyduring acute phase response to tissue injury, infection, or otherinflammatory stimuli. CRP displays several functions associated withhost defense: it promotes agglutination, bacterial capsular swelling,phagocytosis and complement fixation through its calcium-dependentbinding to phosphorylcholine.

TREM1: Triggering receptor expressed on myeloid cells 1; additionalaliases of TREM1 are CD354 and TREM-1. This gene encodes a receptorbelonging to the Ig superfamily that is expressed on myeloid cells. Thisprotein amplifies neutrophil and monocyte-mediated inflammatoryresponses triggered by bacterial and fungal infections by stimulatingrelease of pro-inflammatory chemokines and cytokines, as well asincreased surface expression of cell activation markers. Alternativelyspliced transcript variants encoding different isoforms have been notedfor this gene. The protein encoded by this gene has a soluble form whichis denoted by sTREM 1.

PCT: Procalcitonin (PCT) is a peptide precursor of the hormonecalcitonin, the latter being involved with calcium homeostasis. Thelevels of procalcitonin rise in a response to a proinflammatorystimulus.

SAA: encodes a member of the serum amyloid A family of apolipoproteins.The encoded protein is a major acute phase protein that is highlyexpressed in response to inflammation and tissue injury. This proteinalso plays an important role in HDL metabolism and cholesterolhomeostasis. High levels of this protein are associated with chronicinflammatory diseases including atherosclerosis, rheumatoid arthritis,Alzheimer's disease and Crohn s disease. This protein may also be apotential biomarker for certain tumors. Alternate splicing results inmultiple transcript variants that encode the same protein.

IL6: This gene encodes a cytokine that functions in inflammation and thematuration of B cells. In addition, the encoded protein has been shownto be an endogenous pyrogen capable of inducing fever in people withautoimmune diseases or infections. The protein is primarily produced atsites of acute and chronic inflammation, where it is secreted into theserum and induces a transcriptional inflammatory response throughinterleukin 6 receptor, alpha. The functioning of this gene isimplicated in a wide variety of inflammation-associated disease states,including suspectibility to diabetes mellitus and systemic juvenilerheumatoid arthritis (provided by RefSeq).

ARG1: Arginase catalyzes the hydrolysis of arginine to ornithine andurea. At least two isoforms of mammalian arginase exist (types I and II)which differ in their tissue distribution, subcellular localization,immunologic crossreactivity and physiologic function. The type I isoformencoded by this gene, is a cytosolic enzyme and expressed predominantlyin the liver as a component of the urea cycle. Inherited deficiency ofthis enzyme results in argininemia, an autosomal recessive disordercharacterized by hyperammonemia (provided by RefSeq).

ARPC2: This gene encodes one of seven subunits of the human Arp2/3protein complex. The Arp2/3 protein complex has been implicated in thecontrol of actin polymerization in cells and has been conserved throughevolution. The exact role of the protein encoded by this gene, the p34subunit, has yet to be determined. Two alternatively spliced variantshave been characterized to date. Additional alternatively splicedvariants have been described but their full length nature has not beendetermined (provided by RefSeq).

ATP6V0B: H⁺-ATPase (vacuolar ATPase, V-ATPase) is an enzyme transporterthat functions to acidify intracellular compartments in eukaryoticcells. It is ubiquitously expressed and is present in endomembraneorganelles such as vacuoles, lysosomes, endosomes, the Golgi apparatus,chromaffin granules and coated vesicles, as well as in the plasmamembrane. H⁺-ATPase is a multi-subunit complex composed of two domains.The V1 domain is responsible for ATP hydrolysis and the V0 domain isresponsible for protein translocation. There are two main mechanisms ofregulating H⁺-ATPase activity; recycling of H⁺-ATPase-containingvesicles to and from the plasma membrane and glucose-sensitiveassembly/disassembly of the holo-enzyme complex. These transporters playan important role in processes such as receptor-mediated endocytosis,protein degradation and coupled transport. They have a function in bonereabsorption and mutations in the A3 gene cause recessive osteopetrosis.Furthermore, H⁺-ATPases have been implicated in tumor metastasis andregulation of sperm motility and maturation.

BRI3BP: Involved in tumorigenesis and may function by stabilizingp53/TP53.

CCL19: This gene is one of several CC cytokine genes clustered on thep-arm of chromosome 9. Cytokines are a family of secreted proteinsinvolved in immunoregulatory and inflammatory processes. The CCcytokines are proteins characterized by two adjacent cysteines.

The cytokine encoded by this gene may play a role in normal lymphocyterecirculation and homing. It also plays an important role in traffickingof T cells in thymus, and in T cell and B cell migration to secondarylymphoid organs. It specifically binds to chemokine receptor CCR7(provided by RefSeq).

CES1: Involved in the detoxification of xenobiotics and in theactivation of ester and amide prodrugs. Hydrolyzes aromatic andaliphatic esters, but has no catalytic activity toward amides or a fattyacyl-CoA ester. Hydrolyzes the methyl ester group of cocaine to formbenzoylecgonine. Catalyzes the transesterification of cocaine to formcocaethylene. Displays fatty acid ethyl ester synthase activity,catalyzing the ethyl esterification of oleic acid to ethyloleate.

CORO1A: May be a crucial component of the cytoskeleton of highly motilecells, functioning both in the invagination of large pieces of plasmamembrane, as well as in forming protrusions of the plasma membraneinvolved in cell locomotion. In mycobacteria-infected cells, itsretention on the phagosomal membrane prevents fusion between phagosomesand lysosomes.

HERC5: Major E3 ligase for ISG15 conjugation. Acts as a positiveregulator of innate antiviral response in cells induced by interferon.Makes part of the ISGylation machinery that recognizes target proteinsin a broad and relatively non-specific manner. Catalyzes ISGylation ofIRF3 which results in sustained activation. It attenuates IRF3-PIN1interaction, which antagonizes IRF3 ubiquitination and degradation, andboosts the antiviral response. Catalyzes ISGylation of influenza A viralNSI which attenuates virulence; ISGylated NS 1 fails to form homodimersand thus to interact with its RNA targets. It catalyzes ISGylation ofpapillomavirus type 16 L1 protein which results in dominant-negativeeffect on virus infectivity. Physically associated with polyribosomes,broadly modifies newly synthesized proteins in a co-translationalmanner. In an interferon-stimulated cell, newly translated viralproteins are primary targets of ISG15.

IFI6: This gene was first identified as one of the many genes induced byinterferon. The encoded protein may play a critical role in theregulation of apoptosis. A mini satellite that consists of 26 repeats ofa 12 nucleotide repeating element resembling the mammalian splice donorconsensus sequence begins near the end of the second exon. Alternativelyspliced transcript variants that encode different isoforms by using thetwo downstream repeat units as splice donor sites have been described.

IFIT3: Additional aliases of the protein include without limitation:interferon-induced protein with tetratricopeptide repeats 3, IFI60,ISG60 and Interferon-induced 60 kDa protein.

MBOAT2: Acyltransferase which mediates the conversion oflysophosphatidyl-ethanolamine (1-acyl-sn-glycero-3-phosphoethanolamineor LPE) into phosphatidyl-ethanolamine(1,2-diacyl-sn-glycero-3-phosphoethanolamine or PE) (LPEAT activity).Catalyzes also the acylation of lysophosphatidic acid (LPA) intophosphatidic acid (PA) (LPAAT activity). Has also a very weaklysophosphatidyl-choline acyltransferase (LPCAT activity). Prefersoleoyl-CoA as the acyl donor. Lysophospholipid acyltransferases (LPLATs)catalyze the reacylation step of the phospholipid remodeling pathwayalso known as the Lands cycle.

MX1/MXA: myxovirus (influenza virus) resistance 1; additional aliases ofMX1 include without limitation IFI-78K, IFI78, MX and MxA. In mouse, theinterferon-inducible Mx protein is responsible for a specific antiviralstate against influenza virus infection. The protein encoded by thisgene is similar to the mouse protein as determined by its antigenicrelatedness, induction conditions, physicochemical properties, and aminoacid analysis. This cytoplasmic protein is a member of both the dynaminfamily and the family of large GTPases.

OAS2: This gene encodes a member of the 2-5A synthetase family,essential proteins involved in the innate immune response to viralinfection. The encoded protein is induced by interferons and usesadenosine triphosphate in 2′-specific nucleotidyl transfer reactions tosynthesize 2′,5′-oligoadenylates (2-5As). These molecules activatelatent RNase L, which results in viral RNA degradation and theinhibition of viral replication. The three known members of this genefamily are located in a cluster on chromosome 12. Alternatively splicedtranscript variants encoding different isoforms have been described.

KIAA0082 (FTSJD2): S-adenosyl-L-methionine-dependent methyltransferasethat mediates mRNA cap1 2′-O-ribose methylation to the 5′-cap structureof mRNAs. Methylates the ribose of the first nucleotide of am(7)GpppG-capped mRNA to produce m(7)GpppNmp (cap1).

Cap1 modification is linked to higher levels of translation. May beinvolved in the interferon.

LIPT1: The process of transferring lipoic acid to proteins is a two-stepprocess. The first step is the activation of lipoic acid bylipoate-activating enzyme to form lipoyl-AMP. For the second step, theprotein encoded by this gene transfers the lipoyl moiety to apoproteins.

Alternative splicing in the 5′ UTR of this gene results in fivetranscript variants that encode the same protein. (provided by RefSeq).

LRDD: The protein encoded by this gene contains a leucine-rich repeatand a death domain. This protein has been shown to interact with otherdeath domain proteins, such as Fas (TNFRSF6)-associated via death domain(FADD) and MAP-kinase activating death domain-containing protein (MADD),and thus may function as an adaptor protein in cell death-relatedsignaling processes. The expression of the mouse counterpart of thisgene has been found to be positively regulated by the tumor suppressorp53 and to induce cell apoptosis in response to DNA damage, whichsuggests a role for this gene as an effector of p53-dependent apoptosis.

Alternative splicing results in multiple transcript variants.

MCP-2: This gene is one of several cytokine genes clustered on the q-armof chromosome 17. Cytokines are a family of secreted proteins involvedin immunoregulatory and inflammatory processes. The protein encoded bythis gene is structurally related to the CXC subfamily of cytokines.Members of this subfamily are characterized by two cysteines separatedby a single amino acid. This cytokine displays chemotactic activity formonocytes, lymphocytes, basophils and eosinophils. By recruitingleukocytes to sites of inflammation this cytokine may contribute totumor-associated leukocyte infiltration and to the antiviral stateagainst HIV infection (provided by RefSeq).

PARP9: Poly (ADP-ribose) polymerase (PARP) catalyzes thepost-translational modification of proteins by the addition of multipleADP-ribose moieties. PARP transfers ADP-ribose from nicotinamidedinucleotide (NAD) to glu/asp residues on the substrate protein, andalso polymerizes ADP-ribose to form long/branched chain polymers. PARPinhibitors are being developed for use in a number of pathologiesincluding cancer, diabetes, stroke and cardiovascular diseases.

PTEN: Tumor suppressor. Acts as a dual-specificity protein phosphatase,ephosphorylating tyrosine-, serine- and threonine-phosphorylatedproteins. Also acts as a lipid phosphatase, removing the phosphate inthe D3 position of the inositol ring from phosphatidylinositol (PI)3,4,5-trisphosphate, PI 3,4-diphosphate, PI 3-phosphate and inositol1,3,4,5-tetrakisphosphate with order of substrate preference in vitroPtdIns(3,4,5)P3>PtdIns(3,4)P2>PtdIns3P>Ins(1,3,4,5)P4. The lipidphosphatase activity is critical for its tumor suppressor function.Antagonizes the PI3K-AKT/PKB signaling pathway by dephosphorylatingphosphoinositides and thereby modulating cell cycle progression and cellsurvival. The un-phosphorylated form cooperates with AIP1 to suppressAKT1 activation. Dephosphorylates tyrosine-phosphorylated focal adhesionkinase and inhibits cell migration and integrin-mediated cell spreadingand focal adhesion formation. Plays a role as a key modulator of theAKT-mTOR signaling pathway controlling the tempo of the process ofnewborn neurons integration during adult neurogenesis, including correctneuron positioning, dendritic development and synapse formation. May bea negative regulator of insulin signaling and glucose metabolism inadipose tissue. The nuclear monoubiquitinated form possesses greaterapoptotic potential, whereas the cytoplasmic nonubiquitinated forminduces less tumor suppressive ability.

OARS: Aminoacyl-tRNA synthetases catalyze the aminoacylation of tRNA bytheir cognate amino acid. Because of their central role in linking aminoacids with nucleotide triplets contained in tRNAs, aminoacyl-tRNAsynthetases are thought to be among the first proteins that appeared inevolution. In metazoans, 9 aminoacyl-tRNA synthetases specific forglutamine (gln), glutamic acid (glu), and 7 other amino acids areassociated within a multienzyme complex.

Although present in eukaryotes, glutaminyl-tRNA synthetase (QARS) isabsent from many prokaryotes, mitochondria, and chloroplasts, in whichGln-tRNA(Gln) is formed by transamidation of the misacylatedGlu-tRNA(Gln). Glutaminyl-tRNA synthetase belongs to the class-Iaminoacyl-tRNA synthetase family.

RAB13: could participate in polarized transport, in the assembly and/orthe activity of tight junctions.

RPL34: Ribosomes, the organelles that catalyze protein synthesis,consist of a small 40S subunit and a large 60S subunit. Together thesesubunits are composed of 4 RNA species and approximately 80 structurallydistinct proteins. This gene encodes a ribosomal protein that is acomponent of the 60S subunit. The protein belongs to the L34E family ofribosomal proteins. It is located in the cytoplasm. This gene originallywas thought to be located at 17q21, but it has been mapped to 4q.Transcript variants derived from alternative splicing, alternativetranscription initiation sites, and/or alternative polyadenylationexist; these variants encode the same protein. As is typical for genesencoding ribosomal proteins, there are multiple processed pseudogenes ofthis gene dispersed through the genome.

SART3: The protein encoded by this gene is an RNA-binding nuclearprotein that is a tumor-rejection antigen. This antigen possesses tumorepitopes capable of inducing HLA-A24-restricted and tumor-specificcytotoxic T lymphocytes in cancer patients and may be useful forspecific immunotherapy. This gene product is found to be an importantcellular factor for HIV-1 gene expression and viral replication. It alsoassociates transiently with U6 and U4/U6 snRNPs during the recyclingphase of the spliceosome cycle. This encoded protein is thought to beinvolved in the regulation of mRNA splicing.

TRIM22: Interferon-induced antiviral protein involved in cell innateimmunity. The antiviral activity could in part be mediated byTRIM22-dependent ubiquitination of viral proteins. Plays a role inrestricting the replication of HIV-1, encephalomyocarditis virus (EMCV)and hepatitis B virus (HBV). Acts as a transcriptional repressor of HBVcore promoter. May have E3 ubiquitin-protein ligase activity.

UBE2N: The UBE2V1-UBE2N and UBE2V2-UBE2N heterodimers catalyze thesynthesis of non-canonical ‘Lys-63’-linked polyubiquitin chains. Thistype of polyubiquitination does not lead to protein degradation by theproteasome. It mediates transcriptional activation of target genes. Itplays a role in the control of progress through the cell cycle anddifferentiation.

Plays a role in the error-free DNA repair pathway and contributes to thesurvival of cells after DNA damage. Acts together with the E3 ligases,HLTF and SHPRH, in the ‘Lys-63’-linked poly ubiquitination of PCNA upongenotoxic stress, which is required for DNA repair. It appears to acttogether with E3 ligase RNF5 in the ‘Lys-63’-linked polyubiquitinationof JKAMP thereby regulating JKAMP function by decreasing its associationwith components of the proteasome and ERAD.

XAF1: Seems to function as a negative regulator of members of the IAP(inhibitor of apoptosis protein) family. Inhibits anti-caspase activityof BIRC4. Induces cleavage and inactivation of BIRC4 independent ofcaspase activation. Mediates TNF-alpha-induced apoptosis and is involvedin apoptosis in trophoblast cells. May inhibit BIRC4 indirectly byactivating the mitochondrial apoptosis pathway. After translocation tomitochondra, promotes translocation of BAX to mitochondria andcytochrome c release from mitochondria. Seems to promote theredistribution of BIRC4 from the cytoplasm to the nucleus, probablyindependent of BIRC4 inactivation which seems to occur in the cytoplasm.The BIRC4-XAF1 complex mediates down-regulation of BIRC5/survivin; theprocess requires the E3 ligase activity of BIRC4. Seems to be involvedin cellular sensitivity to the proapoptotic actions of TRAIL. May be atumor suppressor by mediating apoptosis resistance of cancer cells.

ZBP1: DLM1 encodes a Z-DNA binding protein. Z-DNA formation is a dynamicprocess, largely controlled by the amount of supercoiling. May play arole in host defense against tumors and pathogens. Binds Z-DNA (Bysimilarity).

IL11: The protein encoded by this gene is a member of the gp130 familyof cytokines. These cytokines drive the assembly of multisubunitreceptor complexes, all of which contain at least one molecule of thetransmembrane signaling receptor IL6ST (gp130). This cytokine is shownto stimulate the T-cell-dependent development ofimmunoglobulin-producing B cells. It is also found to support theproliferation of hematopoietic stem cells and megakaryocyte progenitorcells.

IL1RA: The protein encoded by this gene is a cytokine receptor thatbelongs to the interleukin 1 receptor family. This protein is a receptorfor interleukin alpha (IL1A), interleukin beta (IL1B), and interleukin 1receptor, type I (IL1R1/IL1RA). It is an important mediator involved inmany cytokine induced immune and inflammatory responses. Additionalnames of the gene include without limitations: CD121A, IL-1RT1, p80,CD121a antigen, CD121A, IL1R and IL1ra.

IP10: This gene encodes a chemokine of the CXC subfamily and ligand forthe receptor CXCR3. Binding of this protein to CXCR3 results inpleiotropic effects, including stimulation of monocytes, natural killerand T-cell migration, and modulation of adhesion molecule expression.Additional names of the gene include without limitations: CXCL10,Gamma-IP10, INP10 and chemokine (C—X—C motif) ligand 10.

I-TAC: Chemotactic for interleukin-activated T-cells but notunstimulated T-cells, neutrophils or monocytes. Induces calcium releasein activated T-cells. Binds to CXCR3. May play an important role in CNSdiseases which involve T-cell recruitment. Additional names of the geneinclude without limitations: SCYB11, SCYB9B and CXCL11.

TNFR1: Receptor for TNFSF2/TNF-alpha and homotrimericTNFSF1/lymphotoxin-alpha. The adapter molecule FADD recruits caspase-8to the activated receptor. The resulting death-inducing signalingcomplex (DISC) performs caspase-8 proteolytic activation which initiatesthe subsequent cascade of caspases (aspartate-specific cysteineproteases) mediating apoptosis. Additional names of the gene includewithout limitations: TNFRSF1A, TNFAR, p55, p60, CD120a antigen andCD120a antigen.

IL-8: The protein encoded by this gene is a member of the CXC chemokinefamily. Additional aliases of IL-8 include without limitation:Interleukin 8, K60, CXCL8, SCYB8, GCP-1, TSG-1, MDNCF, b-ENAP, MONAP,alveolar macrophage chemotactic factor I, NAP-1, beta endothelialcell-derived neutrophil activating peptide, GCP1,beta-thromboglobulin-like protein, LECT, chemokine (C—X—C motif) ligand8, LUCT, emoctakin, LYNAP, interleukin-8, NAF, lung giant cellcarcinoma-derived chemotactic protein, NAP1, lymphocyte derivedneutrophil activating peptide, IL-8, neutrophil-activating peptide 1,Granulocyte chemotactic protein 1, small inducible cytokine subfamily B,member 8, Monocyte-derived neutrophil chemotactic factor, tumor necrosisfactor-induced gene 1, Monocyte-derived neutrophil-activating peptide,Emoctakin, T-cell chemotactic factor, C—X—C motif chemokine 8, 3-10C,Neutrophil-activating protein 1, AMCF-I and Protein 3-10C. Thischemokine is one of the major mediators of the inflammatory response.This chemokine is secreted by several cell types. It functions as achemoattractant, and is also a potent angiogenic factor. This gene isbelieved to play a role in the pathogenesis of bronchiolitis, a commonrespiratory tract disease caused by viral infection. This gene and otherten members of the CXC chemokine gene family form a chemokine genecluster in a region mapped to chromosome 4q. (provided by RefSeq, July2008).

IL-8 is a chemotactic factor that attracts neutrophils, basophils, andT-cells, but not monocytes. It is also involved in neutrophilactivation. IL-8(6-77) has a 5-10-fold higher activity on neutrophilactivation, IL-8(5-77) has increased activity on neutrophil activationand IL-8(7-77) has a higher affinity to receptors CXCR1 and CXCR2 ascompared to IL-8(1-77), respectively.

Definitions

“DETERMINANTS” in the context of the present invention encompass,without limitation, polypeptides, peptide, proteins, protein isoforms(e.g. decoy receptor isoforms), and metabolites. DETERMINANTS can alsoinclude mutated proteins. “DETERMINANT” OR “DETERMINANTS” encompass oneor more of all polypeptides or whose levels are changed in subjects whohave an infection. Individual DETERMINANTS include TRAIL, IL1RA, IP10,Mac-2BP, B2M, BCA-1, CH13L1, Eotaxin, IL1a, MCP, CD62L, VEGFR2, CHP,CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134, CD182,CD231, CD235A, CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR,GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1, IgGnon-specific bound molecules, IL1, I-TAC, TNFR1, IFITM3, IFIT3, EIF4B,IFIT1, LOC26010, MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A, IFITM1,IL7, CRP, SAA, TREM-1, PCT, IL-8, TREM-1, IL6, ARG1, ARPC2, ATP6V0B,BCA-1, BRI3BP, CCL19-MIP3b, CES1, CORO1A, HERC5, IFI6, IFIT3, KIAA0082,LIPT1, LRDD, MCP-2, PARP9, PTEN, QARS, RAB13, RPL34, SART3, TRIM22,UBE2N, XAF1 and ZBP1 and are collectively referred to herein as, interalia, “infection-associated proteins” or “infection-associatedpolypeptides”, “DETERMINANT-polypeptides”, “polypeptide-DETERMINANTS”,“DETERMINANT-proteins” or “protein-DETERMINANTS”.

DETERMINANTS also encompass non-polypeptide, non-blood borne factors ornon-analyte physiological markers of health status referred to hereinas, inter alia, “clinical-DETERMINANTS” or “clinical DETERMINANTS”.

DETERMINANTS also include any calculated indices created mathematicallyor combinations of any one or more of the foregoing measurements,including temporal trends and differences. Where available, and unlessotherwise described herein, DETERMINANTS, which are gene products areidentified based on the official letter abbreviation or gene symbolassigned by the international Human Genome Organization Naming Committee(HGNC) and listed at the date of this filing at the US National Centerfor Biotechnology Information (NCBI) web site(http://wwwdotncbidotnlmdotnihdotgov/sites/entrez?db=gene), also knownas Entrez Gene.

“Clinical-DETERMINANTS” encompass non-polypeptide, non-blood bornefactors or non-analyte physiological markers of health status including“clinical parameters” defined herein, as well as “traditional laboratoryrisk factors”, also defined herein.

“Traditional laboratory risk factors” encompass biomarkers isolated orderived from subject samples and which are currently evaluated in theclinical laboratory and used in traditional global risk assessmentalgorithms, such as absolute neutrophil count (abbreviated ANC),absolute lymphocyte count (abbreviated ALC), white blood count(abbreviated WBC), neutrophil % (defined as the fraction of white bloodcells that are neutrophils and abbreviated Neu (%)), lymphocyte %(defined as the fraction of white blood cells that are lymphocytes andabbreviated Lym (%)), monocyte % (defined as the fraction of white bloodcells that are monocytes and abbreviated Mon (%)), Sodium (abbreviatedNa), Potassium (abbreviated K), Bilirubin (abbreviated Bili).

“Clinical parameters” encompass all non-sample or non-analyte biomarkersof subject health status or other characteristics, such as, withoutlimitation, age (Age), ethnicity (RACE), gender (Sex), core bodytemperature (abbreviated “temperature”), maximal core body temperaturesince initial appearance of symptoms (abbreviated “maximaltemperature”), time from initial appearance of symptoms (abbreviated“time from symptoms”) or family history (abbreviated FamHX).

“soluble-DETERMINANTS”, “secreted-DETERMINANTS” and “solublepolypeptides” are polypeptide-DETERMINANTS that exist outside thecellular interior in different body fluids such as serum, plasma, urine,CSF, sputum, sweat, stool, seminal fluid, etc.

“intracellular-DETERMINANTS”, “intracellular proteins” and“intracellular polypeptides” are polypeptides that are present within acell.

“membrane-DETERMINANTS”, “membrane proteins” and “intracellulardeterminants” are polypeptides that are present on the cell surface ormembrane.

An “Infection Reference Expression Profile,” is a set of valuesassociated with two or more DETERMINANTS resulting from evaluation of abiological sample (or population or set of samples).

A “Subject with non-infectious disease” is one whose disease is notcaused by an infectious disease agent (e.g. bacteria or virus). In thestudy presented herein this includes patients with acute myocardialinfarction, physical injury, epileptic attack etc.

An “Acute Infection” is characterized by rapid onset of disease, arelatively brief period of symptoms, and resolution within days.

A “chronic infection” is an infection that develops slowly and lasts along time. Viruses that may cause a chronic infection include HepatitisC and HIV. One difference between acute and chronic infection is thatduring acute infection the immune system often produces IgM+ antibodiesagainst the infectious agent, whereas the chronic phase of the infectionis usually characteristic of IgM−/IgG+ antibodies. In addition, acuteinfections cause immune mediated necrotic processes while chronicinfections often cause inflammatory mediated fibrotic processes andscaring (e.g. Hepatitis C in the liver). Thus, acute and chronicinfections may elicit different underlying immunological mechanisms.

By infection type is meant to include bacterial infections, mixedinfections, viral infections, no infection, infectious ornon-infectious.

By “ruling in” an infection it is meant that the subject has that typeof infection.

By “ruling out” an infection it is meant that the subject does not havethat type of infection.

The “natural flora”, or “colonizers” refers to microorganisms, such asbacteria or viruses, that may be present in healthy a-symptomaticsubjects and in sick subjects.

An “anti-viral treatment” includes the administration of a compound,drug, regimen or an action that when performed by a subject with a viralinfection can contribute to the subject's recovery from the infection orto a relief from symptoms. Examples of anti-viral treatments includewithout limitation the administration of the following drugs:oseltamivir, RNAi antivirals, monoclonal antibody respigams, zanamivir,and neuriminidase blocking agents.

“TP” is true positive, means positive test result that accuratelyreflects the tested-for activity. For example in the context of thepresent invention a TP, is for example but not limited to, trulyclassifying a bacterial infection as such.

“TN” is true negative, means negative test result that accuratelyreflects the tested-for activity. For example in the context of thepresent invention a TN, is for example but not limited to, trulyclassifying a viral infection as such.

“FN” is false negative, means a result that appears negative but failsto reveal a situation. For example in the context of the presentinvention a FN, is for example but not limited to, falsely classifying abacterial infection as a viral infection.

“FP” is false positive, means test result that is erroneously classifiedin a positive category. For example in the context of the presentinvention a FP, is for example but not limited to, falsely classifying aviral infection as a bacterial infection.

“Sensitivity” is calculated by TP/(TP+FN) or the true positive fractionof disease subjects.

“Specificity” is calculated by TN/(TN+FP) or the true negative fractionof non-disease or normal subjects.

“Total accuracy” is calculated by (TN+TP)/(TN+FP+TP+FN).

“Positive predictive value” or “PPV” is calculated by TP/(TP+FP) or thetrue positive fraction of all positive test results. It is inherentlyimpacted by the prevalence of the disease and pre-test probability ofthe population intended to be tested.

“Negative predictive value” or “NPV” is calculated by TN/(TN+FN) or thetrue negative fraction of all negative test results. It also isinherently impacted by the prevalence of the disease and pre-testprobability of the population intended to be tested. See, e.g.,O'Marcaigh A S, Jacobson R M, “Estimating The Predictive Value Of ADiagnostic Test, How To Prevent Misleading Or Confusing Results,” Clin.Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, andpositive and negative predictive values of a test, e.g., a clinicaldiagnostic test.

“MCC” (Mathwes Correlation coefficient) is calculated as follows:MCC=(TP*TN−FP*FN)/{(TP+FN)*(TP+FP)*(TN+FP)*(TN+FN)}^0.5 where TP, FP,TN, FN are true-positives, false-positives, true-negatives, andfalse-negatives, respectively. Note that MCC values range between −1 to+1, indicating completely wrong and perfect classification,respectively. An MCC of 0 indicates random classification. MCC has beenshown to be a useful for combining sensitivity and specificity into asingle metric (Baldi, Brunak et al. 2000). It is also useful formeasuring and optimizing classification accuracy in cases of unbalancedclass sizes (Baldi, Brunak et al. 2000).

Often, for binary disease state classification approaches using acontinuous diagnostic test measurement, the sensitivity and specificityis summarized by a Receiver Operating Characteristics (ROC) curveaccording to Pepe et al, “Limitations of the Odds Ratio in Gauging thePerformance of a Diagnostic, Prognostic, or Screening Marker,” Am. J.Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under theCurve (AUC) or c-statistic, an indicator that allows representation ofthe sensitivity and specificity of a test, assay, or method over theentire range of test (or assay) cut points with just a single value. Seealso, e.g., Shultz, “Clinical Interpretation Of Laboratory Procedures,”chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis andAshwood (eds.), 4^(th) edition 1996, W.B. Saunders Company, pages192-199; and Zweig et al., “ROC Curve Analysis: An Example Showing TheRelationships Among Serum Lipid And Apolipoprotein Concentrations InIdentifying Subjects With Coronory Artery Disease,” Clin. Chem., 1992,38(8): 1425-1428. An alternative approach using likelihood functions,odds ratios, information theory, predictive values, calibration(including goodness-of-fit), and reclassification measurements issummarized according to Cook, “Use and Misuse of the Receiver OperatingCharacteristic Curve in Risk Prediction,” Circulation 2007, 115:928-935.

“Accuracy” refers to the degree of conformity of a measured orcalculated quantity (a test reported value) to its actual (or true)value. Clinical accuracy relates to the proportion of true outcomes(true positives (TP) or true negatives (TN) versus misclassifiedoutcomes (false positives (FP) or false negatives (FN)), and may bestated as a sensitivity, specificity, positive predictive values (PPV)or negative predictive values (NPV), Matheus correlation coefficient(MCC), or as a likelihood, odds ratio, Receiver Operating Characteristic(ROC) curve, Area Under the Curve (AUC) among other measures.

A “formula,” “algorithm,” or “model” is any mathematical equation,algorithmic, analytical or programmed process, or statistical techniquethat takes one or more continuous or categorical inputs (herein called“parameters”) and calculates an output value, sometimes referred to asan “index” or “index value”. Non-limiting examples of “formulas” includesums, ratios, and regression operators, such as coefficients orexponents, biomarker value transformations and normalizations(including, without limitation, those normalization schemes based onclinical-DETERMINANTS, such as gender, age, or ethnicity), rules andguidelines, statistical classification models, and neural networkstrained on historical populations. Of particular use in combiningDETERMINANTS are linear and non-linear equations and statisticalclassification analyses to determine the relationship between levels ofDETERMINANTS detected in a subject sample and the subject's probabilityof having an infection or a certain type of infection. In panel andcombination construction, of particular interest are structural andsyntactic statistical classification algorithms, and methods of indexconstruction, utilizing pattern recognition features, includingestablished techniques such as cross-correlation, Principal ComponentsAnalysis (PCA), factor rotation, Logistic Regression (Log Reg), LinearDiscriminant Analysis (LDA), Eigengene Linear Discriminant Analysis(ELDA), Support Vector Machines (SVM), Random Forest (RF), RecursivePartitioning Tree (RPART), as well as other related decision treeclassification techniques, Shrunken Centroids (SC), StepAIC, Kth-NearestNeighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks,and Hidden Markov Models, among others. Other techniques may be used insurvival and time to event hazard analysis, including Cox, Weibull,Kaplan-Meier and Greenwood models well known to those of skill in theart. Many of these techniques are useful either combined with aDETERMINANT selection technique, such as forward selection, backwardsselection, or stepwise selection, complete enumeration of all potentialpanels of a given size, genetic algorithms, or they may themselvesinclude biomarker selection methodologies in their own technique. Thesemay be coupled with information criteria, such as Akaike's InformationCriterion (AIC) or Bayes Information Criterion (BIC), in order toquantify the tradeoff between additional biomarkers and modelimprovement, and to aid in minimizing overfit. The resulting predictivemodels may be validated in other studies, or cross-validated in thestudy they were originally trained in, using such techniques asBootstrap, Leave-One-Out (LOO) and 10-Fold cross-validation (10-FoldCV). At various steps, false discovery rates may be estimated by valuepermutation according to techniques known in the art. A “health economicutility function” is a formula that is derived from a combination of theexpected probability of a range of clinical outcomes in an idealizedapplicable patient population, both before and after the introduction ofa diagnostic or therapeutic intervention into the standard of care. Itencompasses estimates of the accuracy, effectiveness and performancecharacteristics of such intervention, and a cost and/or valuemeasurement (a utility) associated with each outcome, which may bederived from actual health system costs of care (services, supplies,devices and drugs, etc.) and/or as an estimated acceptable value perquality adjusted life year (QALY) resulting in each outcome. The sum,across all predicted outcomes, of the product of the predictedpopulation size for an outcome multiplied by the respective outcome'sexpected utility is the total health economic utility of a givenstandard of care. The difference between (i) the total health economicutility calculated for the standard of care with the intervention versus(ii) the total health economic utility for the standard of care withoutthe intervention results in an overall measure of the health economiccost or value of the intervention. This may itself be divided amongstthe entire patient group being analyzed (or solely amongst theintervention group) to arrive at a cost per unit intervention, and toguide such decisions as market positioning, pricing, and assumptions ofhealth system acceptance. Such health economic utility functions arecommonly used to compare the cost-effectiveness of the intervention, butmay also be transformed to estimate the acceptable value per QALY thehealth care system is willing to pay, or the acceptable cost-effectiveclinical performance characteristics required of a new intervention.

For diagnostic (or prognostic) interventions of the invention, as eachoutcome (which in a disease classifying diagnostic test may be a TP, FP,TN, or FN) bears a different cost, a health economic utility functionmay preferentially favor sensitivity over specificity, or PPV over NPVbased on the clinical situation and individual outcome costs and value,and thus provides another measure of health economic performance andvalue which may be different from more direct clinical or analyticalperformance measures. These different measurements and relativetrade-offs generally will converge only in the case of a perfect test,with zero error rate (a.k.a., zero predicted subject outcomemisclassifications or FP and FN), which all performance measures willfavor over imperfection, but to differing degrees.

“Measuring” or “measurement,” or alternatively “detecting” or“detection,” means assessing the presence, absence, quantity or amount(which can be an effective amount) of either a given substance within aclinical or subject-derived sample, including the derivation ofqualitative or quantitative concentration levels of such substances, orotherwise evaluating the values or categorization of a subject'snon-analyte clinical parameters or clinical-DETERMINANTS.

“Analytical accuracy” refers to the reproducibility and predictabilityof the measurement process itself, and may be summarized in suchmeasurements as coefficients of variation (CV), Pearson correlation, andtests of concordance and calibration of the same samples or controlswith different times, users, equipment and/or reagents. These and otherconsiderations in evaluating new biomarkers are also summarized inVasan, 2006.

“Performance” is a term that relates to the overall usefulness andquality of a diagnostic or prognostic test, including, among others,clinical and analytical accuracy, other analytical and processcharacteristics, such as use characteristics (e.g., stability, ease ofuse), health economic value, and relative costs of components of thetest. Any of these factors may be the source of superior performance andthus usefulness of the test, and may be measured by appropriate“performance metrics,” such as AUC and MCC, time to result, shelf life,etc. as relevant.

A “sample” in the context of the present invention is a biologicalsample isolated from a subject and can include, by way of example andnot limitation, whole blood, serum, plasma, saliva, mucus, breath,urine, CSF, sputum, sweat, stool, hair, seminal fluid, biopsy,rhinorrhea, tissue biopsy, cytological sample, platelets, reticulocytes,leukocytes, epithelial cells, or whole blood cells.

By “statistically significant”, it is meant that the alteration isgreater than what might be expected to happen by chance alone (whichcould be a “false positive”). Statistical significance can be determinedby any method known in the art. Commonly used measures of significanceinclude the p-value, which presents the probability of obtaining aresult at least as extreme as a given data point, assuming the datapoint was the result of chance alone. A result is often consideredhighly significant at a p-value of 0.05 or less.

A “subject” in the context of the present invention is preferably ahuman. A subject can be male or female. A subject can be one who hasbeen previously diagnosed or identified as having an infection, andoptionally has already undergone, or is undergoing, a therapeuticintervention for the infection. Alternatively, a subject can also be onewho has not been previously diagnosed as having an infection. Forexample, a subject can be one who exhibits one or more risk factors forhaving an infection.

In the context of the present invention the following abbreviations maybe used: Antibiotics (Abx), Adverse Event (AE), Arbitrary Units (A.U.),Complete Blood Count (CBC), Case Report Form (CRF), Chest X-Ray (CXR),Electronic Case Report Form (eCRF), Food and Drug Administration (FDA),Good Clinical Practice (GCP), Gastrointestinal (GI), Gastroenteritis(GE), International Conference on Harmonization (ICH), InfectiousDisease (ID), In vitro diagnostics (IVD), Lower Respiratory TractInfection (LRTI), Myocardial infarction (MI), Polymerase chain reaction(PCR), Per-oss (P.O), Per-rectum (P.R), Standard of Care (SoC), StandardOperating Procedure (SOP), Urinary Tract Infection (UTI), UpperRespiratory Tract Infection (URTI).

Methods and Uses of the Invention

The methods disclosed herein are used to identify subjects with aninfection or a specific infection type. By type of infection it is meantto include bacterial infections, viral infections, mixed infections, noinfection (i.e., non-infectious) More specifically, some methods of theinvention are used to distinguish subjects having a bacterial infection,a viral infection, a mixed infection (i.e., bacterial and viralco-infection), patients with a non-infectious disease and healthyindividuals. Some methods of the present invention can also be used tomonitor or select a treatment regimen for a subject who has a aninfection, and to screen subjects who have not been previously diagnosedas having an infection, such as subjects who exhibit risk factorsdeveloping an infection. Some methods of the present invention are usedto identify and/or diagnose subjects who are asymptomatic for aninfection. “Asymptomatic” means not exhibiting the traditional signs andsymptoms.

The term “Gram-positive bacteria” are bacteria that are stained darkblue by Gram staining. Gram-positive organisms are able to retain thecrystal violet stain because of the high amount of peptidoglycan in thecell wall.

The term “Gram-negative bacteria” are bacteria that do not retain thecrystal violet dye in the Gram staining protocol.

The term “Atypical bacteria” are bacteria that do not fall into one ofthe classical “Gram” groups. They are usually, though not always,intracellular bacterial pathogens. They include, without limitations,Mycoplasmas spp., Legionella spp. Rickettsiae spp., and Chlamydiae spp.

As used herein, infection is meant to include any infectious agent ofviral or bacterial origin. The bacterial infection may be the result ofgram-positive, gram-negative bacteria or atypical bacteria.

A subject having an infection is identified by measuring the amounts(including the presence or absence) of an effective number (which can beone or more) of DETERMINANTS in a subject-derived sample. A clinicallysignificant alteration in the level of the DETERMINANT is determined.Alternatively, the amounts are compared to a reference value.

Alterations in the amounts and patterns of expression DETERMINANTS inthe subject sample compared to the reference value are then identified.In various embodiments, two, three, four, five, six, seven, eight, nine,ten or more DETERMINANTS are measured. For example, the combination ofDETERMINANTS may be selected according to any of the models enumeratedin Tables 2-3.

In some embodiments the combination of DETERMINANTS comprisemeasurements of one or more polypeptides selected from the groupconsisting of TRAIL, IL1RA, IP10, Mac-2BP, B2M, BCA-1, CHI3L1, Eotaxin,IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1,RTN3, CD112, CD134, CD182, CD231, CD235A, CD335, CD337, CD45, CD49D,CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B,SELI, SPINT2, SSEA1, IgG non-specific bound molecules, IL1, I-TAC andTNFR1.

In some embodiments the combination of DETERMINANTS comprisemeasurements of one or more soluble-polypeptides selected from the groupconsisting of B2M, BCA-1, CHI3L1, Eotaxin, IL1a, IL1RA, IP10, MCP,Mac-2BP, TRAIL, CD62L and VEGFR2.

In some embodiments the combination of DETERMINANTS comprisemeasurements of one or more intracellular-polypeptides selected from thegroup consisting of CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1 andRTN3.

In some embodiments the combination of DETERMINANTS comprisemeasurements of one or more membrane-polypeptides selected from thegroup consisting of TRAIL, CD112, CD134, CD182, CD231, CD235A, CD335,CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C,ITGAM, NRG1, RAP1B, SELI, SPINT2 and SSEA1.

In some embodiments, the polypeptides measurements further comprisemeasurements of one or more polypeptides selected from the groupconsisting of EIF4B, IFIT1, IFIT3, LOC26010, MBOAT2, MX1, OAS2, RSAD2,ADIPOR1, CD15, CD8A, IFITM1, IFITM3, IL7R, CRP, SAA, sTREM, PCT, IL-8and IL6.

In some embodiments, the polypeptides measurements further comprisemeasurements of one or more clinical-DETERMINANTS selected from thegroup consisting of ANC, ALC, Neu (%), Lym (%), Mono (%), Maximaltemperature, Time from symptoms, Age, Creatinine (Cr), Potassium (K),Pulse and Urea.

In some embodiments, the polypeptides or clinical-DETERMINANTSmeasurements further comprise measurements of one or more polypeptide orclinical-DETERMINANTS selected from the group consisting of ARG1, ARPC2,ATP6V0B, BILI (Bilirubin), BRI3BP, CCL19-MIP3B, CES1, CORO1A, EOS (%),HERC5, IFI6, IFIT3, KIAA0082, LIPT1, LRDD, MCP-2, NA (Sodium), PARP9,PTEN, QARS, RAB13, RPL34, SART3, TRIM22, UBE2N, WBC (Whole Blood Count),XAF1 and ZBP1.

In various aspects the method distinguishes a virally infected subjectfrom either a subject with non-infectious disease or a healthy subject;a bacterially infected subject, from either a subject withnon-infectious disease or a healthy subject; a subject with aninfectious disease from either a subject with an non-infectious diseaseor a healthy subject; a bacterially infected subject from a virallyinfected subject; a mixed infected subject from a virally infectedsubject; a mixed infected subject from a bacterially infected subjectand a bacterially or mixed infected and subject from a virally infectedsubject.

For example, the invention provides a method of identifying the type ofinfection in a subject by measuring the levels of a first DETERMINANTselected from the group consisting of TRAIL, IL1RA, IP10, Mac-2BP, B2M,BCA-1, CHI3L1, Eotaxin, IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C,EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134, CD182, CD231, CD235A,CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR, GPR162,HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1, IgG non-specificbound molecules, IL1, I-TAC and TNFR1 in a sample from the subject; andmeasuring the levels of a second DETERMINANT. The second DETERMINANT isselected from TRAIL, IL1RA, IP10, Mac-2BP, B2M, BCA-1, CHI3L1, Eotaxin,IL1a, MCP, CD62L, VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1,RTN3, CD112, CD134, CD182, CD231, CD235A, CD335, CD337, CD45, CD49D,CD66A/C/D/E, CD73, CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B,SELI, SPINT2, SSEA1, IgG non-specific bound molecules, IL1, I-TAC andTNFR1; IFITM3, IFIT3, EIF4B, IFIT1, LOC26010, MBOAT2, MX1, OAS2, RSAD2,ADIPOR1, CD15, CD8A, IFITM1, and IL7; CRP, SAA, TREM-1, PCT, IL-8,TREM-1 and IL6;Age, absolute neutrophil count (ANC), absolute lymphocytecount (ALC), neutrophil % (Neu (%)), lymphocyte % (Lym (%)), monocyte %(Mono (%)), Maximal temperature, Time from symptoms, Creatinine (Cr),Potassium (K), Pulse and Urea. The levels of the first and secondDETERMINANTS is compared to a reference value thereby identifying thetype of infection in the subject wherein the measurement of the secondDETERMINANT increases the accuracy of the identification of the type ofinfection over the measurement of the first DETERMINANT alone.Optionally, one or more additional DETERMINANTS selected from TRAIL,IL1RA, IP10, Mac-2BP, B2M, BCA-1, CHI3L1, Eotaxin, IL1a, MCP, CD62L,VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134,CD182, CD231, CD235A, CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73,CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1,IgG non-specific bound molecules, IL1, I-TAC and TNFR1; IFITM3, IFIT3,EIF4B, IFIT1, LOC26010, MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A,IFITM1, and IL7; CRP, SAA, TREM-1, PCT, IL-8, TREM-1 and IL6; Age,absolute neutrophil count (ANC), absolute lymphocyte count (ALC),neutrophil % (Neu (%)), lymphocyte % (Lym (%)), monocyte % (Mono (%)),Maximal temperature, Time from symptoms, Creatinine (Cr), Potassium (K),Pulse and Urea are measured. The measurement of the additionalDETERMINANTS increases the accuracy of the identification of the type ofinfection over the measurement of the first and second DETERMINANTS.

In preferred embodiments the following DETERMINANTS are measured:

B2M is measured and a second DETERMINANT selected from the groupconsisting of BCA-1, CHI3L1, Eotaxin, IL1a, IP10, MCP, Mac-2BP, TRAIL,sCD62L, VEGFR2, CRP, SAA, TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym(%), Mono (%), Maximal temperature, Time from symptoms, Age, Creatinine(Cr), Potassium (K), Pulse and Urea is measured;

BCA-1 is measured and a second DETERMINANT selected from the groupconsisting of, CHI3L1, Eotaxin, IL1a, IP10, MCP, Mac-2BP, TRAIL, CD62L,VEGFR2, CRP, SAA, TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym (%),Mono (%), Maximal temperature, Time from symptoms, Age, Creatinine (Cr),Potassium (K), Pulse and Urea is measured;

CHI3L1 is measured and a second DETERMINANT selected from the groupconsisting of Eotaxin, IL1a, IP10, MCP, Mac-2BP, TRAIL, CD62L, VEGFR2,CRP, SAA, TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym (%), Mono (%),Maximal temperature, Time from symptoms, Age, Creatinine (Cr), Potassium(K), Pulse and Urea is measured;

Eotaxin is measured and a second DETERMINANT selected from the groupconsisting of IL1a, IP10, MCP, Mac-2BP, TRAIL, CD62L, VEGFR2, CRP, SAA,TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym (%), Mono (%), Maximaltemperature, Time from symptoms, Age, Creatinine (Cr), Potassium (K),Pulse and Urea is measured;

IL1a is measured and a second DETERMINANT selected from the groupconsisting of IP10, MCP, Mac-2BP, TRAIL, CD62L, VEGFR2, CRP, SAA,TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym (%), Mono (%), Maximaltemperature, Time from symptoms, Age, Creatinine (Cr), Potassium (K),Pulse and Urea is measured;

IP10 is measured and a second DETERMINANT selected from the groupconsisting of MCP, Mac-2BP, TRAIL, CD62L, VEGFR2, CRP, SAA, TREM-1, PCT,IL-8, IL6, ANC, ALC, Neu (%), Lym (%), Mono (%), Maximal temperature,Time from symptoms, Age, Creatinine (Cr), Potassium (K), Pulse and Ureais measured;

MCP is measured and a second DETERMINANT selected from the groupconsisting of Mac-2BP, TRAIL, CD62L, VEGFR2, CRP, SAA, TREM-1, PCT,IL-8, IL6, ANC, ALC, Neu (%), Lym (%), Mono (%), Maximal temperature,Time from symptoms, Age, Creatinine (Cr), Potassium (K), Pulse and Ureais measured;

Mac-2BP is measured and a second DETERMINANT selected from the groupconsisting of TRAIL, CD62L, VEGFR2, CRP, SAA, TREM-1, PCT, IL-8, IL6,ANC, ALC, Neu (%), Lym (%), Mono (%), Maximal temperature, Time fromsymptoms, Age, Creatinine (Cr), Potassium (K), Pulse and Urea ismeasured;

TRAIL is measured and a second DETERMINANT selected from the groupconsisting of CD62L, VEGFR2, CRP, TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu(%), Lym (%), Mono (%), Maximal temperature, Time from symptoms, Age,Creatinine (Cr), Potassium (K), Pulse and Urea is measured;

CD62L is measured and a second DETERMINANT selected from the groupconsisting of VEGFR2, CRP, SAA, TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu(%), Lym (%), Mono (%), Maximal temperature, Time from symptoms, Age,Creatinine (Cr), Potassium (K), Pulse and Urea is measured;

VEGFR2 is measured and a second DETERMINANT selected from the groupconsisting of CRP, SAA, TREM-1, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym(%), Mono (%), Maximal temperature, Time from symptoms, Age, Creatinine(Cr), Potassium (K), Pulse and Urea is measured; or

TREM-1 is measured and a second DETERMINANT selected from the groupconsisting of CRP, PCT, IL-8, IL6, ANC, ALC, Neu (%), Lym (%), Mono (%),Maximal temperature, Time from symptoms, Age, Creatinine (Cr), Potassium(K), Pulse and Urea is measured.

In one aspect the method distinguishes a bacterially infected subjectfrom a virally infected subject by measuring one or more DETERMINANTSselected from B2M, BCA-1, CHI3L1, Eotaxin, IL1RA, IP10, MCP, Mac-2BP,TRAIL, CD62L and VEGFR2 are measured and one or more DETERMINANTSselected from the group consisting of CRP, TREM-1, SAA, PCT, IL-8, IL6,ANC, ALC, Neu (%), Lym (%), Mono (%), Maximal temperature, Time fromsymptoms, Age, Creatinine (Cr), Potassium (K), Pulse and Urea. Forexample, CRP and TRAIL are measured; CRP and TRAIL and SAA are measured;CRP and TRAIL and Mac-2BP are measured; CRP and TRAIL and PCT and aremeasured; CRP and TRAIL and SAA and Mac-2BP are measured; PCT and TRAILare measured; or SAA and TRAIL are measured. In a another aspect themethod distinguishes between a mixed infected subject and a virallyinfected subject by measuring wherein one or more DETERMINANTS selectedfrom TRAIL, IP10, IL1RA, CHI3L1, CMPK2 and MCP-2 are measured andoptionally one or more DETERMINANTS selected from the group consistingof CRP, SAA, ANC, ATP6V0B, CES1, CORO1A, HERC5, IFITM1, LIPT1, LOC26010,LRDD, Lym (%), MCP-2, MX1, Neu (%), OAS2, PARP9, RSAD2, SART3, WBC, PCT,IL-8, IL6 and TREM-1.

In another aspect the method distinguishes between a bacterial or mixedinfected subject and a virally infected subject by measuring wherein oneor more DETERMINANTS selected from TRAIL, IL1RA, IP10, ARG1, CD337,CD73, CD84, CHI3L1, CHP, CMPK2, CORO1C, EIF2AK2, Eotaxin, GPR162,HLA-A/B/C, ISG15, ITGAM, Mac-2BP, NRG1, RAP1B, RPL22L1, SSEA1, RSAD2,RTN3, SELI, VEGFR2, CD62L and VEGFR2 are measured and optionally one ormore DETERMINANTS selected from the group consisting of CRP, SAA, PCT,IL6, IL8, ADIPOR1, ANC, Age, B2M, Bili total, CD15, Cr, EIF4B, IFIT1,IFIT3, IFITM1, IL7R, K (potassium), KIAA0082, LOC26010, Lym (%), MBOAT2,MCP-2, MX1, Na, Neu (%), OAS2, PARP9, PTEN, Pulse, Urea, WBC, ZBP1,mIgG1 and TREM-1.

In another aspect the method distinguishes between a subject with aninfectious disease and a subject with a non-infectious disease or ahealthy subject by measuring one or more DETERMINANTS selected fromIP10, IL1RA, TRAIL, BCA-1, CCL19-MIP3b, CES1 and CMPK2. Optionally, oneor more DETERMINANTS selected from CRP, SAA, PCT, IL6, IL8, ARPC2,ATP6V0B, Cr, Eos (%), HERC5, IFI6, IFIT3, KIAA0082, LIPT1, LOC26010,LRDD, MBOAT2, MX1, Maximal temperature, OAS2, PARP9, Pulse, QARS, RAB13,RPL34, RSAD2, SART3, RIM22, UBE2N, XAF1, IL11, I-TAC and TNFR1 aremeasured.

In specific embodiments the invention includes determining if a subjectdoes not have a bacterial infection (i.e. ruling out a bacterialinfection). A bacterial infection is ruled out if the polypeptideconcentration of TRAIL determined is higher than a pre-determined firstthreshold value. Optionally, the method further includes determining ifa subject has a viral infection (i.e., ruling in a viral infection). Aviral infection is rule in if the polypeptide concentration of TRAIL ishigher than a pre-determined second threshold value.

In another specific embodiment the invention includes determining if asubject does not have a viral infection (i.e. ruling out a viralinfection). A viral infection is ruled out if the polypeptideconcentration of TRAIL determined is lower than a pre-determined firstthreshold value. Optionally, the method further includes determining ifa subject has a bacterial infection (i.e., ruling in a bacterialinfection). A bacterial infection is rule in if the polypeptideconcentration of TRAIL is lower than a pre-determined second thresholdvalue.

In other embodiments the invention includes a method of distinguishingbetween a bacterial infection and a viral infection in a subject bymeasuring the polypeptide concentration of TRAIL and CRP in a subjectderived sample, applying a pre-determined mathematical function on theconcentrations of TRAIL and CRP to compute a score and comparing thescore to a predetermined reference value. Optionally, one or more ofSAA, PCT, B2M Mac-2BP, IL1RA or IP10 is measured.

In another embodiment, the invention provides a method of distinguishingbetween a bacterial or mixed infection, and a viral infection in asubject by measuring the polypeptide concentration of TRAIL and CRP in asubject derived sample, applying a pre-determined mathematical functionon the concentrations of TRAIL and CRP to compute a score and comparingthe score to a predetermined reference value. Optionally, one or more ofSAA, PCT, B2M Mac-2BP, IL1RA or IP10 is measured.

For example to distinguish between a bacterial infection and a viralinfection or bacterial or mixed infection, and a viral infection TRAIL,CRP and SAA are measured; TRAIL, CRP and IP10 are measured; TRAIL, CRPand PCT are measured; TRAIL, CRP and IL1RA are measured; TRAIL, CRP andB2M are measured; TRAIL, CRP and Mac-2BP are measured; TRAIL, CRP, SAAand PCT are measured; TRAIL, CRP, Mac-2BP and SAA are measured; TRAIL,CRP, SAA and IP10 are measured; TRAIL, CRP, SAA and IL1RA are measured;TRAIL, CRP, SAA, PCT and IP10 are measured; TRAIL, CRP, SAA, PCT andIL1RA are measured; or TRAIL, CRP, SAA, IP10 and IL1RA are measured.

A reference value can be relative to a number or value derived frompopulation studies, including without limitation, such subjects havingthe same infection, subject having the same or similar age range,subjects in the same or similar ethnic group, or relative to thestarting sample of a subject undergoing treatment for an infection. Suchreference values can be derived from statistical analyses and/or riskprediction data of populations obtained from mathematical algorithms andcomputed indices of infection. Reference DETERMINANT indices can also beconstructed and used using algorithms and other methods of statisticaland structural classification.

In one embodiment of the present invention, the reference value is theamount (i.e. level) of DETERMINANTS in a control sample derived from oneor more subjects who do not have an infection (i.e., healthy, and ornon-infectious individuals). In a further embodiment, such subjects aremonitored and/or periodically retested for a diagnostically relevantperiod of time (“longitudinal studies”) following such test to verifycontinued absence of infection. Such period of time may be one day, twodays, two to five days, five days, five to ten days, ten days, or ten ormore days from the initial testing date for determination of thereference value. Furthermore, retrospective measurement of DETERMINANTSin properly banked historical subject samples may be used inestablishing these reference values, thus shortening the study timerequired.

A reference value can also comprise the amounts of DETERMINANTS derivedfrom subjects who show an improvement as a result of treatments and/ortherapies for the infection. A reference value can also comprise theamounts of DETERMINANTS derived from subjects who have confirmedinfection by known techniques.

In another embodiment, the reference value is an index value or abaseline value. An index value or baseline value is a composite sampleof an effective amount of DETERMINANTS from one or more subjects who donot have an infection. A baseline value can also comprise the amounts ofDETERMINANTS in a sample derived from a subject who has shown animprovement in treatments or therapies for the infection. In thisembodiment, to make comparisons to the subject-derived sample, theamounts of DETERMINANTS are similarly calculated and compared to theindex value. Optionally, subjects identified as having an infection, arechosen to receive a therapeutic regimen to slow the progression oreliminate the infection.

Additionally, the amount of the DETERMINANT can be measured in a testsample and compared to the “normal control level,” utilizing techniquessuch as reference limits, discrimination limits, or risk definingthresholds to define cutoff points and abnormal values. The “normalcontrol level” means the level of one or more DETERMINANTS or combinedDETERMINANT indices typically found in a subject not suffering from aninfection. Such normal control level and cutoff points may vary based onwhether a DETERMINANT is used alone or in a formula combining with otherDETERMINANTS into an index. Alternatively, the normal control level canbe a database of DETERMINANT patterns from previously tested subjects.

The effectiveness of a treatment regimen can be monitored by detecting aDETERMINANT in an effective amount (which may be one or more) of samplesobtained from a subject over time and comparing the amount ofDETERMINANTS detected. For example, a first sample can be obtained priorto the subject receiving treatment and one or more subsequent samplesare taken after or during treatment of the subject.

For example, the methods of the invention can be used to discriminatebetween bacterial, viral and mixed infections (i.e. bacterial and viralco-infections.) This will allow patients to be stratified and treatedaccordingly.

In a specific embodiment of the invention a treatment recommendation(i.e., selecting a treatment regimen) for a subject is provided bymeasuring the polypeptide concentration of TRAIL in a subject derivedsample; and recommending that the subject receives an antibiotictreatment if polypeptide concentration of TRAIL is lower than apre-determined threshold value; recommending that the patient does notreceive an antibiotic treatment if the polypeptide concentration ofTRAIL is higher than a pre-determined threshold value; or recommendingthat the patient receive an anti-viral treatment if the polypeptideconcentration of TRAIL determined in step (a) is higher than apre-determined threshold value.

In another specific embodiment of the invention a treatmentrecommendation (i.e., selecting a treatment regimen) for a subject isprovided by identifying the type infection (i.e., bacterial, viral,mixed infection or no infection) in the subject according to the methodof any of the disclosed methods and recommending that the subjectreceive an antibiotic treatment if the subject is identified as havingbacterial infection or a mixed infection; or an anti-viral treatment isif the subject is identified as having a viral infection.

In another embodiment, the methods of the invention can be used toprompt additional targeted diagnosis such as pathogen specific PCRs,chest-X-ray, cultures etc. For example, a reference value that indicatesa viral infection, may prompt the usage of additional viral specificmultiplex-PCRs, whereas a reference value that indicates a bacterialinfection may prompt the usage of a bacterial specific multiplex-PCR.Thus, one can reduce the costs of unwarranted expensive diagnostics.

In a specific embodiment, a diagnostic test recommendation for a subjectis provided by measuring the polypeptide concentration of TRAIL in asubject derived sample; and recommending testing the sample for abacteria if the polypeptide concentration of TRAIL is lower than apre-determined threshold value; or recommending testing the sample for avirus if the polypeptide concentration of TRAIL is higher than apre-determined threshold value.

In another specific embodiment, a diagnostic test recommendation for asubject is provided by identifying the infection type (i.e., bacterial,viral, mixed infection or no infection) in the subject according to anyof the disclosed methods.

Recommending a test to determine the source of the bacterial infectionif the subject is identified as having a bacterial infection or a mixedinfection; or a test to determine the source of the viral infection ifthe subject is identified as having a viral infection.

Some aspects of the present invention also comprise a kit with adetection reagent that binds to one or more DETERMINANT. Also providedby the invention is an array of detection reagents, e.g., antibodiesthat can bind to one or more DETERMINANT-polypeptides. In oneembodiment, the DETERMINANTS are polypeptides and the array containsantibodies that bind one or more DETERMINANTS selected from TRAIL,IL1RA, IP10, Mac-2BP, B2M, BCA-1, CHI3L1, Eotaxin, IL1a, MCP, CD62L,VEGFR2, CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134,CD182, CD231, CD235A, CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73,CD84, EGFR, GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1,IgG non-specific bound molecules, IL1, I-TAC, TNFR1, IFITM3, IFIT3,EIF4B, IFIT1, LOC26010, MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A,IFITM1, 1L7, CRP, SAA, TREM-1, PCT, IL-8, TREM-1, IL6, ARG1, ARPC2,ATP6V0B, BCA-1, BRI3BP, CCL19-MIP3b, CES1, CORO1A, HERC5, IFI6, IFIT3,KIAA0082, LIPT1, LRDD, MCP-2, PARP9, PTEN, QARS, RAB13, RPL34, SART3,TRIM22, UBE2N, XAF1 and ZBP1 sufficient to measure a statisticallysignificant alteration in DETERMINANT expression.

Preferably, the concentration of the polypeptide-DETERMINANTS ismeasured within about 24 hours after sample is obtained. Alternatively,the concentration of the polypeptide-DETERMINANTS measured in a samplethat was stored at 12° C. or lower, when storage begins less than 24hours after the sample is obtained.

In another embodiment the DETERMINANT is TRAIL and the array containsantibodies that bind TRAIL. In another embodiment the DETERMINANTS areTRAIL and CRP and the array contains antibodies that bind TRAIL and CRP.In another embodiment the DETERMINANTS are TRAIL, CRP and VEGFR2 and thearray contains antibodies that bind TRAIL, CRP and VEGFR2. In anotherembodiment the DETERMINANTS are TRAIL, CRP and Mac2-BP and the arraycontains antibodies that bind TRAIL, CRP and Mac2-BP. In anotherembodiment the DETERMINANTS are TRAIL, CRP, VEGFR2 and Mac2-BP and thearray contains antibodies that bind TRAIL, CRP, VEGFR2 and Mac2-BP. Inanother embodiment the DETERMINANTS are TRAIL, CRP and SAA and the arraycontains antibodies that bind TRAIL, CRP and SAA. In another embodimentthe DETERMINANTS are TRAIL, CRP, SAA and Mac2-BP and the array containsantibodies that bind TRAIL, CRP, SAA and Mac2-BP. In another embodimentthe DETERMINANTS are TRAIL, CRP, SAA and IL1RA and the array containsantibodies that bind TRAIL, CRP, SAA and IL1RA. The levels ofDETERMINANT in different types of infections are depicted in FIGS.21-22. Our findings that TRAIL concentrations in viral infected patientsare higher than bacterial infected patients (median of 121±132 pg/mlversus 52±65 pg/ml), support the embodiments wherein TRAILconcentrations are measured. Furthermore, when we monitored TRAILconcentrations over time in patients infected with a virus, we found asubstantial increase in concentrations shortly after the infection,followed by a gradual decrease and returning to basal levels (forexample see FIG. 41). More examples of TRAIL concentrations in differentinfections are presented in FIGS. 35-39. Interestingly, we find thatcombining TRAIL levels, which are higher in viral compared to bacterialinfections, and CRP levels, which are higher in bacterial compared toviral infections, enables a diagnostic accuracy that is superior to anyof the individual biomarkers. For example, we found that combining thelevels of CRP and TRAIL by computing a pre-determined mathematicalformula produces a score that diagnoses the source of infection moreaccurately then each of the biomarkers individually (TRAIL AUC=0.89, CRPAUC=0.89, TRAIL and CRP combined AUC=0.94). For example see FIGS. 23-24visualizes a linear formula that is used to incorporate the levels ofTRAIL and CRP into a single score. Other formulas exist, which are knownto someone skilled in the art. The TRAIL and CRP diagnostic synergism,may be attributed to the low correlation between these two biomarkers.We observe similar results when combining the concentrations of SAA andTRAIL (for example see FIGS. 23-24).

We compared the genomic sequence of TRAIL across different organismsusing the UCSC genome browser (Human Feb. 2009 (GRCh37/hg19) assembly,and found that it is evolutionary conserved (especially in the exonregions) (see FIG. 42). For example, we find sequence conservation inlarge and small mammals such as cow, horse, dog and cat. This suggeststhat TRAIL may have a similar protein behavior across differentorganisms similar to what we found in human (including up regulation inviral infections).

Of note, TRAIL is highly expressed in other tissues and samplesincluding without limitation CSF, saliva and epithelial cells, bonemarrow aspiration, urine, stool, alveolar lavage, sputum, saliva(Secchiero, Lamberti et al. 2009). Thus, some embodiments of the presentinvention can be used to measure TRAIL in such tissues and samples,wherein an increase of TRAIL concentrations indicate increasedlikelihood of a viral infection.

Some aspects of the present invention can also be used to screen patientor subject populations in any number of settings. For example, a healthmaintenance organization, public health entity or school health programcan screen a group of subjects to identify those requiringinterventions, as described above, or for the collection ofepidemiological data. Insurance companies (e.g., health, life ordisability) may screen applicants in the process of determining coverageor pricing, or existing clients for possible intervention. Datacollected in such population screens, particularly when tied to anyclinical progression to conditions like infection, will be of value inthe operations of, for example, health maintenance organizations, publichealth programs and insurance companies. Such data arrays or collectionscan be stored in machine-readable media and used in any number ofhealth-related data management systems to provide improved healthcareservices, cost effective healthcare, improved insurance operation, etc.See, for example, U.S. Patent Application No. 2002/0038227; U.S. PatentApplication No. US 2004/0122296; U.S. Patent Application No. US2004/0122297; and U.S. Pat. No. 5,018,067. Such systems can access thedata directly from internal data storage or remotely from one or moredata storage sites as further detailed herein.

A machine-readable storage medium can comprise a data storage materialencoded with machine readable data or data arrays which, when using amachine programmed with instructions for using said data, is capable ofuse for a variety of purposes. Measurements of effective amounts of thebiomarkers of the invention and/or the resulting evaluation of risk fromthose biomarkers can be implemented in computer programs executing onprogrammable computers, comprising, inter alia, a processor, a datastorage system (including volatile and non-volatile memory and/orstorage elements), at least one input device, and at least one outputdevice. Program code can be applied to input data to perform thefunctions described above and generate output information. The outputinformation can be applied to one or more output devices, according tomethods known in the art. The computer may be, for example, a personalcomputer, microcomputer, or workstation of conventional design.

Each program can be implemented in a high level procedural or objectoriented programming language to communicate with a computer system.However, the programs can be implemented in assembly or machinelanguage, if desired. The language can be a compiled or interpretedlanguage. Each such computer program can be stored on a storage media ordevice (e.g., ROM or magnetic diskette or others as defined elsewhere inthis disclosure) readable by a general or special purpose programmablecomputer, for configuring and operating the computer when the storagemedia or device is read by the computer to perform the proceduresdescribed herein. The health-related data management system used in someaspects of the invention may also be considered to be implemented as acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform various functions describedherein.

The DETERMINANTS of the present invention, in some embodiments thereof,can be used to generate a “reference DETERMINANT profile” of thosesubjects who do not have an infection. The DETERMINANTS disclosed hereincan also be used to generate a “subject DETERMINANT profile” taken fromsubjects who have an infection. The subject DETERMINANT profiles can becompared to a reference DETERMINANT profile to diagnose or identifysubjects with an infection. The subject DETERMINANT profile of differentinfection types can be compared to diagnose or identify the type ofinfection. The reference and subject DETERMINANT profiles of the presentinvention, in some embodiments thereof, can be contained in amachine-readable medium, such as but not limited to, analog tapes likethose readable by a VCR, CD-ROM, DVD-ROM, USB flash media, among others.Such machine-readable media can also contain additional test results,such as, without limitation, measurements of clinical parameters andtraditional laboratory risk factors. Alternatively or additionally, themachine-readable media can also comprise subject information such asmedical history and any relevant family history. The machine-readablemedia can also contain information relating to other disease-riskalgorithms and computed indices such as those described herein.

Performance and Accuracy Measures of the Invention

The performance and thus absolute and relative clinical usefulness ofthe invention may be assessed in multiple ways as noted above. Amongstthe various assessments of performance, some aspects of the inventionare intended to provide accuracy in clinical diagnosis and prognosis.The accuracy of a diagnostic or prognostic test, assay, or methodconcerns the ability of the test, assay, or method to distinguishbetween subjects having an infection is based on whether the subjectshave, a “significant alteration” (e.g., clinically significant anddiagnostically significant) in the levels of a DETERMINANT. By“effective amount” it is meant that the measurement of an appropriatenumber of DETERMINANTS (which may be one or more) to produce a“significant alteration” (e.g. level of expression or activity of aDETERMINANT) that is different than the predetermined cut-off point (orthreshold value) for that DETERMINANT(S) and therefore indicates thatthe subject has an infection for which the DETERMINANT(S) is adeterminant. The difference in the level of DETERMINANT is preferablystatistically significant. As noted below, and without any limitation ofthe invention, achieving statistical significance, and thus thepreferred analytical, diagnostic, and clinical accuracy, may requirethat combinations of several DETERMINANTS be used together in panels andcombined with mathematical algorithms in order to achieve astatistically significant DETERMINANT index.

In the categorical diagnosis of a disease state, changing the cut pointor threshold value of a test (or assay) usually changes the sensitivityand specificity, but in a qualitatively inverse relationship. Therefore,in assessing the accuracy and usefulness of a proposed medical test,assay, or method for assessing a subject's condition, one should alwaystake both sensitivity and specificity into account and be mindful ofwhat the cut point is at which the sensitivity and specificity are beingreported because sensitivity and specificity may vary significantly overthe range of cut points. One way to achieve this is by using the MCCmetric, which depends upon both sensitivity and specificity. Use ofstatistics such as AUC, encompassing all potential cut point values, ispreferred for most categorical risk measures when using some aspects ofthe invention, while for continuous risk measures, statistics ofgoodness-of-fit and calibration to observed results or other goldstandards, are preferred.

By predetermined level of predictability it is meant that the methodprovides an acceptable level of clinical or diagnostic accuracy. Usingsuch statistics, an “acceptable degree of diagnostic accuracy”, isherein defined as a test or assay (such as the test used in some aspectsof the invention for determining the clinically significant presence ofDETERMINANTS, which thereby indicates the presence an infection type) inwhich the AUC (area under the ROC curve for the test or assay) is atleast 0.60, desirably at least 0.65, more desirably at least 0.70,preferably at least 0.75, more preferably at least 0.80, and mostpreferably at least 0.85.

By a “very high degree of diagnostic accuracy”, it is meant a test orassay in which the AUC (area under the ROC curve for the test or assay)is at least 0.75, 0.80, desirably at least 0.85, more desirably at least0.875, preferably at least 0.90, more preferably at least 0.925, andmost preferably at least 0.95.

Alternatively, the methods predict the presence or absence of aninfection or response to therapy with at least 75% total accuracy, morepreferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater total accuracy.

Alternatively, the methods predict the presence or absence of aninfection or response to therapy with an MCC larger than 0.2, 0.3, 0.4,0.5, 0.6, 0.7, 0.8., 0.9 or 1.0.

The predictive value of any test depends on the sensitivity andspecificity of the test, and on the prevalence of the condition in thepopulation being tested. This notion, based on Bayes' theorem, providesthat the greater the likelihood that the condition being screened for ispresent in an individual or in the population (pre-test probability),the greater the validity of a positive test and the greater thelikelihood that the result is a true positive. Thus, the problem withusing a test in any population where there is a low likelihood of thecondition being present is that a positive result has limited value(i.e., more likely to be a false positive). Similarly, in populations atvery high risk, a negative test result is more likely to be a falsenegative.

As a result, ROC and AUC can be misleading as to the clinical utility ofa test in low disease prevalence tested populations (defined as thosewith less than 1% rate of occurrences (incidence) per annum, or lessthan 10% cumulative prevalence over a specified time horizon).

A health economic utility function is an yet another means of measuringthe performance and clinical value of a given test, consisting ofweighting the potential categorical test outcomes based on actualmeasures of clinical and economic value for each. Health economicperformance is closely related to accuracy, as a health economic utilityfunction specifically assigns an economic value for the benefits ofcorrect classification and the costs of misclassification of testedsubjects. As a performance measure, it is not unusual to require a testto achieve a level of performance which results in an increase in healtheconomic value per test (prior to testing costs) in excess of the targetprice of the test.

In general, alternative methods of determining diagnostic accuracy arecommonly used for continuous measures, when a disease category has notyet been clearly defined by the relevant medical societies and practiceof medicine, where thresholds for therapeutic use are not yetestablished, or where there is no existing gold standard for diagnosisof the pre-disease. For continuous measures of risk, measures ofdiagnostic accuracy for a calculated index are typically based on curvefit and calibration between the predicted continuous value and theactual observed values (or a historical index calculated value) andutilize measures such as R squared, Hosmer-Lemeshow P-value statisticsand confidence intervals. It is not unusual for predicted values usingsuch algorithms to be reported including a confidence interval (usually90% or 95% CI) based on a historical observed cohort's predictions, asin the test for risk of future breast cancer recurrence commercializedby Genomic Health, Inc. (Redwood City, Calif.).

In general, by defining the degree of diagnostic accuracy, i.e., cutpoints on a ROC curve, defining an acceptable AUC value, and determiningthe acceptable ranges in relative concentration of what constitutes aneffective amount of the DETERMINANTS of the invention allows for one ofskill in the art to use the DETERMINANTS to identify, diagnose, orprognose subjects with a pre-determined level of predictability andperformance.

Furthermore, other unlisted biomarkers will be very highly correlatedwith the DETERMINANTS (for the purpose of this application, any twovariables will be considered to be “very highly correlated” when theyhave a Coefficient of Determination (R2) of 0.5 or greater). Someaspects of the present invention encompass such functional andstatistical equivalents to the aforementioned DETERMINANTS. Furthermore,the statistical utility of such additional DETERMINANTS is substantiallydependent on the cross-correlation between multiple biomarkers and anynew biomarkers will often be required to operate within a panel in orderto elaborate the meaning of the underlying biology.

One or more of the listed DETERMINANTS can be detected in the practiceof the present invention, in some embodiments thereof. For example, two(2), three (3), four (4), five (5), ten (10), fifteen (15), twenty (20),forty (40), or more DETERMINANTS can be detected.

In some aspects, all DETERMINANTS listed herein can be detected.Preferred ranges from which the number of DETERMINANTS can be detectedinclude ranges bounded by any minimum selected from between one and,particularly two, three, four, five, six, seven, eight, nine ten,twenty, or forty. Particularly preferred ranges include two to five(2-5), two to ten (2-10), two to twenty (2-20), or two to forty (2-40).

Construction of DETERMINANT Panels

Groupings of DETERMINANTS can be included in “panels”, also called“DETERMINANT-signatures”, “DETERMINANT signatures”, or“multi-DETERMINANT signatures.” A “panel” within the context of thepresent invention means a group of biomarkers (whether they areDETERMINANTS, clinical parameters, or traditional laboratory riskfactors) that includes one or more DETERMINANTS. A panel can alsocomprise additional biomarkers, e.g., clinical parameters, traditionallaboratory risk factors, known to be present or associated withinfection, in combination with a selected group of the DETERMINANTSlisted herein.

As noted above, many of the individual DETERMINANTS, clinicalparameters, and traditional laboratory risk factors listed, when usedalone and not as a member of a multi-biomarker panel of DETERMINANTS,have little or no clinical use in reliably distinguishing individualnormal subjects, subjects at risk for having an infection (e.g.,bacterial, viral or co-infection), and thus cannot reliably be usedalone in classifying any subject between those three states. Even wherethere are statistically significant differences in their meanmeasurements in each of these populations, as commonly occurs in studieswhich are sufficiently powered, such biomarkers may remain limited intheir applicability to an individual subject, and contribute little todiagnostic or prognostic predictions for that subject. A common measureof statistical significance is the p-value, which indicates theprobability that an observation has arisen by chance alone; preferably,such p-values are 0.05 or less, representing a 5% or less chance thatthe observation of interest arose by chance. Such p-values dependsignificantly on the power of the study performed.

Despite this individual DETERMINANT performance, and the generalperformance of formulas combining only the traditional clinicalparameters and few traditional laboratory risk factors, the presentinventors have noted that certain specific combinations of two or moreDETERMINANTS can also be used as multi-biomarker panels comprisingcombinations of DETERMINANTS that are known to be involved in one ormore physiological or biological pathways, and that such information canbe combined and made clinically useful through the use of variousformulae, including statistical classification algorithms and others,combining and in many cases extending the performance characteristics ofthe combination beyond that of the individual DETERMINANTS. Thesespecific combinations show an acceptable level of diagnostic accuracy,and, when sufficient information from multiple DETERMINANTS is combinedin a trained formula, they often reliably achieve a high level ofdiagnostic accuracy transportable from one population to another.

The general concept of how two less specific or lower performingDETERMINANTS are combined into novel and more useful combinations forthe intended indications, is a key aspect of some embodiments of theinvention. Multiple biomarkers can yield better performance than theindividual components when proper mathematical and clinical algorithmsare used; this is often evident in both sensitivity and specificity, andresults in a greater AUC or MCC. Secondly, there is often novelunperceived information in the existing biomarkers, as such wasnecessary in order to achieve through the new formula an improved levelof sensitivity or specificity. This hidden information may hold trueeven for biomarkers which are generally regarded to have suboptimalclinical performance on their own. In fact, the suboptimal performancein terms of high false positive rates on a single biomarker measuredalone may very well be an indicator that some important additionalinformation is contained within the biomarker results—information whichwould not be elucidated absent the combination with a second biomarkerand a mathematical formula.

Several statistical and modeling algorithms known in the art can be usedto both assist in DETERMINANT selection choices and optimize thealgorithms combining these choices. Statistical tools such as factor andcross-biomarker correlation/covariance analyses allow more rationaleapproaches to panel construction. Mathematical clustering andclassification tree showing the Euclidean standardized distance betweenthe DETERMINANTS can be advantageously used. Pathway informed seeding ofsuch statistical classification techniques also may be employed, as mayrational approaches based on the selection of individual DETERMINANTSbased on their participation across in particular pathways orphysiological functions.

Ultimately, formula such as statistical classification algorithms can bedirectly used to both select DETERMINANTS and to generate and train theoptimal formula necessary to combine the results from multipleDETERMINANTS into a single index. Often, techniques such as forward(from zero potential explanatory parameters) and backwards selection(from all available potential explanatory parameters) are used, andinformation criteria, such as AIC or BIC, are used to quantify thetradeoff between the performance and diagnostic accuracy of the paneland the number of DETERMINANTS used. The position of the individualDETERMINANT on a forward or backwards selected panel can be closelyrelated to its provision of incremental information content for thealgorithm, so the order of contribution is highly dependent on the otherconstituent DETERMINANTS in the panel.

Construction of Clinical Algorithms

Any formula may be used to combine DETERMINANT results into indicesuseful in the practice of the invention. As indicated above, and withoutlimitation, such indices may indicate, among the various otherindications, the probability, likelihood, absolute or relative risk,time to or rate of conversion from one to another disease states, ormake predictions of future biomarker measurements of infection. This maybe for a specific time period or horizon, or for remaining lifetimerisk, or simply be provided as an index relative to another referencesubject population.

Although various preferred formula are described here, several othermodel and formula types beyond those mentioned herein and in thedefinitions above are well known to one skilled in the art. The actualmodel type or formula used may itself be selected from the field ofpotential models based on the performance and diagnostic accuracycharacteristics of its results in a training population. The specificsof the formula itself may commonly be derived from DETERMINANT resultsin the relevant training population. Amongst other uses, such formulamay be intended to map the feature space derived from one or moreDETERMINANT inputs to a set of subject classes (e.g. useful inpredicting class membership of subjects as normal, having an infection),to derive an estimation of a probability function of risk using aBayesian approach, or to estimate the class-conditional probabilities,then use Bayes' rule to produce the class probability function as in theprevious case.

Preferred formulas include the broad class of statistical classificationalgorithms, and in particular the use of discriminant analysis. The goalof discriminant analysis is to predict class membership from apreviously identified set of features. In the case of lineardiscriminant analysis (LDA), the linear combination of features isidentified that maximizes the separation among groups by some criteria.Features can be identified for LDA using an eigengene based approachwith different thresholds (ELDA) or a stepping algorithm based on amultivariate analysis of variance (MANOVA). Forward, backward, andstepwise algorithms can be performed that minimize the probability of noseparation based on the Hotelling-Lawley statistic.

Eigengene-based Linear Discriminant Analysis (ELDA) is a featureselection technique developed by Shen et al. (2006). The formula selectsfeatures (e.g. biomarkers) in a multivariate framework using a modifiedeigen analysis to identify features associated with the most importanteigenvectors. “Important” is defined as those eigenvectors that explainthe most variance in the differences among samples that are trying to beclassified relative to some threshold.

A support vector machine (SVM) is a classification formula that attemptsto find a hyperplane that separates two classes. This hyperplanecontains support vectors, data points that are exactly the margindistance away from the hyperplane. In the likely event that noseparating hyperplane exists in the current dimensions of the data, thedimensionality is expanded greatly by projecting the data into largerdimensions by taking non-linear functions of the original variables(Venables and Ripley, 2002). Although not required, filtering offeatures for SVM often improves prediction. Features (e.g., biomarkers)can be identified for a support vector machine using a non-parametricKruskal-Wallis (KW) test to select the best univariate features. Arandom forest (RF, Breiman, 2001) or recursive partitioning (RPART,Breiman et al., 1984) can also be used separately or in combination toidentify biomarker combinations that are most important. Both KW and RFrequire that a number of features be selected from the total. RPARTcreates a single classification tree using a subset of availablebiomarkers.

Other formula may be used in order to pre-process the results ofindividual DETERMINANT measurement into more valuable forms ofinformation, prior to their presentation to the predictive formula. Mostnotably, normalization of biomarker results, using either commonmathematical transformations such as logarithmic or logistic functions,as normal or other distribution positions, in reference to apopulation's mean values, etc. are all well known to those skilled inthe art. Of particular interest are a set of normalizations based onclinical-DETERMINANTS such as age, time from symptoms, gender, race, orsex, where specific formula are used solely on subjects within a classor continuously combining a clinical-DETERMINANTS as an input. In othercases, analyte-based biomarkers can be combined into calculatedvariables which are subsequently presented to a formula.

In addition to the individual parameter values of one subjectpotentially being normalized, an overall predictive formula for allsubjects, or any known class of subjects, may itself be recalibrated orotherwise adjusted based on adjustment for a population's expectedprevalence and mean biomarker parameter values, according to thetechnique outlined in D'Agostino et al, (2001) JAMA 286:180-187, orother similar normalization and recalibration techniques. Suchepidemiological adjustment statistics may be captured, confirmed,improved and updated continuously through a registry of past datapresented to the model, which may be machine readable or otherwise, oroccasionally through the retrospective query of stored samples orreference to historical studies of such parameters and statistics.Additional examples that may be the subject of formula recalibration orother adjustments include statistics used in studies by Pepe, M. S. etal, 2004 on the limitations of odds ratios; Cook, N. R., 2007 relatingto ROC curves. Finally, the numeric result of a classifier formulaitself may be transformed post-processing by its reference to an actualclinical population and study results and observed endpoints, in orderto calibrate to absolute risk and provide confidence intervals forvarying numeric results of the classifier or risk formula.

Some DETERMINANTS may exhibit trends that depends on the patient age(e.g. the population baseline may rise or fall as a function of age).One can use a ‘Age dependent normalization or stratification’ scheme toadjust for age related differences. Performing age dependentnormalization or stratification can be used to improve the accuracy ofDETERMINANTS for differentiating between different types of infections.For example, one skilled in the art can generate a function that fitsthe population mean levels of each DETERMINANT as function of age anduse it to normalize the DETERMINANT of individual subjects levels acrossdifferent ages. Another example is to stratify subjects according totheir age and determine age specific thresholds or index values for eachage group independently.

Measurement of DETERMINANTS

The actual measurement of levels or amounts of the DETERMINANTS can bedetermined at the protein or polypeptide level using any method known inthe art.

For example, by measuring the levels of polypeptide encoded by the geneproducts described herein, or subcellular localization or activitiesthereof. Such methods are well known in the art and include, e.g.,immunoassays based on antibodies to proteins, aptamers or molecularimprints. Any biological material can be used for thedetection/quantification of the protein or its activity. Alternatively,a suitable method can be selected to determine the activity of proteinsencoded by the marker genes according to the activity of each proteinanalyzed.

The DETERMINANT proteins, polypeptides, mutations, and polymorphismsthereof can be detected in any suitable manner, but is typicallydetected by contacting a sample from the subject with an antibody, whichbinds the DETERMINANT protein, polypeptide, mutation, polymorphism, orpost translational modification additions (e.g. carbohydrates) and thendetecting the presence or absence of a reaction product. The antibodymay be monoclonal, polyclonal, chimeric, or a fragment of the foregoing,as discussed in detail above, and the step of detecting the reactionproduct may be carried out with any suitable immunoassay. The samplefrom the subject is typically a biological sample as described above,and may be the same sample of biological sample used to conduct themethod described above.

Immunoassays carried out in accordance with some embodiments of thepresent invention may be homogeneous assays or heterogeneous assays. Ina homogeneous assay the immunological reaction usually involves thespecific antibody (e.g., anti-DETERMINANT protein antibody), a labeledanalyte, and the sample of interest. The signal arising from the labelis modified, directly or indirectly, upon the binding of the antibody tothe labeled analyte. Both the immunological reaction and detection ofthe extent thereof can be carried out in a homogeneous solution.Immunochemical labels, which may be employed, include free radicals,radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.

In a heterogeneous assay approach, the reagents are usually the sample,the antibody, and means for producing a detectable signal. Samples asdescribed above may be used. The antibody can be immobilized on asupport, such as a bead (such as protein A and protein G agarose beads),plate or slide, and contacted with the specimen suspected of containingthe antigen in a liquid phase. The support is then separated from theliquid phase and either the support phase or the liquid phase isexamined for a detectable signal employing means for producing suchsignal. The signal is related to the presence of the analyte in thesample. Means for producing a detectable signal include the use ofradioactive labels, fluorescent labels, or enzyme labels. For example,if the antigen to be detected contains a second binding site, anantibody which binds to that site can be conjugated to a detectablegroup and added to the liquid phase reaction solution before theseparation step. The presence of the detectable group on the solidsupport indicates the presence of the antigen in the test sample.Examples of suitable immunoassays are oligonucleotides, immunoblotting,immunofluorescence methods, immunoprecipitation, chemiluminescencemethods, electrochemiluminescence (ECL) or enzyme-linked immunoassays.

Those skilled in the art will be familiar with numerous specificimmunoassay formats and variations thereof which may be useful forcarrying out the method disclosed herein. See generally E. Maggio,Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see alsoU.S. Pat. No. 4,727,022 to Skold et al. titled “Methods for ModulatingLigand-Receptor Interactions and their Application,” U.S. Pat. No.4,659,678 to Forrest et al. titled “Immunoassay of Antigens,” U.S. Pat.No. 4,376,110 to David et al., titled “Immunometric Assays UsingMonoclonal Antibodies,” U.S. Pat. No. 4,275,149 to Litman et al., titled“Macromolecular Environment Control in Specific Receptor Assays,” U.S.Pat. No. 4,233,402 to Maggio et al., titled “Reagents and MethodEmploying Channeling,” and U.S. Pat. No. 4,230,767 to Boguslaski et al.,titled “Heterogenous Specific Binding Assay Employing a Coenzyme asLabel.” The DETERMINANT can also be detected with antibodies using flowcytometry. Those skilled in the art will be familiar with flowcytometric techniques which may be useful in carrying out the methodsdisclosed herein (Shapiro 2005). These include, without limitation,Cytokine Bead Array (Becton Dickinson) and Luminex technology.

Antibodies can be conjugated to a solid support suitable for adiagnostic assay (e.g., beads such as protein A or protein G agarose,microspheres, plates, slides or wells formed from materials such aslatex or polystyrene) in accordance with known techniques, such aspassive binding. Antibodies as described herein may likewise beconjugated to detectable labels or groups such as radiolabels (e.g.,35S, 125I, 131I), enzyme labels (e.g., horseradish peroxidase, alkalinephosphatase), and fluorescent labels (e.g., fluorescein, Alexa, greenfluorescent protein, rhodamine) in accordance with known techniques.

Antibodies can also be useful for detecting post-translationalmodifications of DETERMINANT proteins, polypeptides, mutations, andpolymorphisms, such as tyrosine phosphorylation, threoninephosphorylation, serine phosphorylation, glycosylation (e.g., O-GlcNAc).Such antibodies specifically detect the phosphorylated amino acids in aprotein or proteins of interest, and can be used in immunoblotting,immunofluorescence, and ELISA assays described herein. These antibodiesare well-known to those skilled in the art, and commercially available.Post-translational modifications can also be determined using metastableions in reflector matrix-assisted laser desorption ionization-time offlight mass spectrometry (MALDI-TOF) (Wirth U. and Muller D. 2002).

For DETERMINANT-proteins, polypeptides, mutations, and polymorphismsknown to have enzymatic activity, the activities can be determined invitro using enzyme assays known in the art. Such assays include, withoutlimitation, kinase assays, phosphatase assays, reductase assays, amongmany others. Modulation of the kinetics of enzyme activities can bedetermined by measuring the rate constant KM using known algorithms,such as the Hill plot, Michaelis-Menten equation, linear regressionplots such as Lineweaver-Burk analysis, and Scatchard plot.

The term “metabolite” includes any chemical or biochemical product of ametabolic process, such as any compound produced by the processing,cleavage or consumption of a biological molecule (e.g., a protein,nucleic acid, carbohydrate, or lipid). Metabolites can be detected in avariety of ways known to one of skill in the art, including therefractive index spectroscopy (RI), ultra-violet spectroscopy (UV),fluorescence analysis, radiochemical analysis, near-infraredspectroscopy (near-IR), nuclear magnetic resonance spectroscopy (NMR),light scattering analysis (LS), mass spectrometry, pyrolysis massspectrometry, nephelometry, dispersive Raman spectroscopy, gaschromatography combined with mass spectrometry, liquid chromatographycombined with mass spectrometry, matrix-assisted laser desorptionionization-time of flight (MALDI-TOF) combined with mass spectrometry,ion spray spectroscopy combined with mass spectrometry, capillaryelectrophoresis, NMR and IR detection. In this regard, other DETERMINANTanalytes can be measured using the above-mentioned detection methods, orother methods known to the skilled artisan. For example, circulatingcalcium ions (Ca 2+) can be detected in a sample using fluorescent dyessuch as the poly-amino carboxylic acid, Fluo series, Fura-2A, Rhod-2,the ratiometric calcium indicator Indo-1, among others.

Other DETERMINANT metabolites can be similarly detected using reagentsthat are specifically designed or tailored to detect such metabolites.

Kits

Some aspects of the invention also include a DETERMINANT-detectionreagent, or antibodies packaged together in the form of a kit. The kitmay contain in separate containers an antibody (either already bound toa solid matrix or packaged separately with reagents for binding them tothe matrix), control formulations (positive and/or negative), and/or adetectable label such as fluorescein, green fluorescent protein,rhodamine, cyanine dyes, Alexa dyes, luciferase, radiolabels, amongothers. Instructions (e.g., written, tape, VCR, CD-ROM, etc.) forcarrying out the assay may be included in the kit. The assay may forexample be in the form of a sandwich ELISA as known in the art.

For example, DETERMINANT detection reagents can be immobilized on asolid matrix such as a porous strip to form at least one DETERMINANTdetection site. The measurement or detection region of the porous stripmay include a plurality of sites. A test strip may also contain sitesfor negative and/or positive controls. Alternatively, control sites canbe located on a separate strip from the test strip. Optionally, thedifferent detection sites may contain different amounts of immobilizeddetection reagents, e.g., a higher amount in the first detection siteand lesser amounts in subsequent sites. Upon the addition of testsample, the number of sites displaying a detectable signal provides aquantitative indication of the amount of DETERMINANTS present in thesample. The detection sites may be configured in any suitably detectableshape and are typically in the shape of a bar or dot spanning the widthof a test strip.

Suitable sources for antibodies for the detection of DETERMINANTSinclude commercially available sources such as, for example, Abazyme,Abnova, AssayPro, Affinity Biologicals, AntibodyShop, Aviva bioscience,Biogenesis, Biosense Laboratories, Calbiochem, Cell Sciences, ChemiconInternational, Chemokine, Clontech, Cytolab, DAKO, DiagnosticBioSystems, eBioscience, Endocrine Technologies, Enzo Biochem,Eurogentec, Fusion Antibodies, Genesis Biotech, GloboZymes, HaematologicTechnologies, Immunodetect, Immunodiagnostik, Immunometrics, Immunostar,Immunovision, Biogenex, Invitrogen, Jackson ImmunoResearch Laboratory,KMI Diagnostics, Koma Biotech, LabFrontier Life Science Institute, LeeLaboratories, Lifescreen, Maine Biotechnology Services, Mediclone,MicroPharm Ltd., ModiQuest, Molecular Innovations, Molecular Probes,Neoclone, Neuromics, New England Biolabs, Novocastra, Novus Biologicals,Oncogene Research Products, Orbigen, Oxford Biotechnology, Panvera,PerkinElmer Life Sciences, Pharmingen, Phoenix Pharmaceuticals, PierceChemical Company, Polymun Scientific, Polysiences, Inc., PromegaCorporation, Proteogenix, Protos Immunoresearch, QED Biosciences, Inc.,R&D Systems, Repligen, Research Diagnostics, Roboscreen, Santa CruzBiotechnology, Seikagaku America, Serological Corporation, Serotec,SigmaAldrich, StemCell Technologies, Synaptic Systems GmbH, Technopharm,Terra Nova Biotechnology, TiterMax, Trillium Diagnostics, UpstateBiotechnology, US Biological, Vector Laboratories, Wako Pure ChemicalIndustries, and Zeptometrix. However, the skilled artisan can routinelymake antibodies, against any of the polypeptide DETERMINANTS describedherein.

We note that the fraction in which the polypeptide DETERMINANTS resideaffects the ease by which the assay can be performed at the clinicalsetting. For example, in the clinical setting, especially thepoint-of-care, it is often easier to measure polypeptides that arepresent in the serum or plasma fraction compared to intra-cellularpolypeptides within the leukocytes fraction. This is because the latterrequires an additional experimental step in which leukocytes areisolated from the whole blood sample, washed and lysed.

We note that in some clinical settings it is more convenient to applyassays that measure polypeptides, rather than RNA. In particular wefound that RNA levels that are differentially induced in different typesof infections do not necessarily show the same behavior on thepolypeptide level. For example, the mRNAs of IFI44, IFI44L and IFI27have been found to be differentially expressed in viral compared tobacterial infections. However, when we measured and compared theirpolypeptide levels in bacterial versus viral infected patients we didnot observe a significant differential response (FIG. 38).

Examples of “Monoclonal antibodies for measuring TRAIL”, include withoutlimitation: Mouse, Monoclonal (55B709-3) IgG; Mouse, Monoclonal (2E5)IgG1; Mouse, Monoclonal (2E05) IgG1; Mouse, Monoclonal (M912292) IgG1kappa; Mouse, Monoclonal (IIIF6) IgG2b; Mouse, Monoclonal (2E1-1B9)IgG1; Mouse, Monoclonal (RIK-2) IgG1, kappa; Mouse, Monoclonal M181 IgG;Mouse, Monoclonal VI10E IgG2b; Mouse, Monoclonal MAB375 IgG1; Mouse,Monoclonal MAB687 IgG1; Mouse, Monoclonal HS501 IgG1; Mouse, Monoclonalclone 75411.11 Mouse IgG1; Mouse, Monoclonal T8175-50 IgG; Mouse,Monoclonal 2B2.108 IgG1; Mouse, Monoclonal B-T24 IgG1; Mouse, Monoclonal55B709.3 IgG1; Mouse, Monoclonal D3 IgG1; Goat, Monoclonal C19 IgG;Rabbit, Monoclonal H257 IgG; Mouse, Monoclonal 500-M49 IgG; Mouse,Monoclonal 05-607 IgG; Mouse, Monoclonal B-T24 IgG1; Rat, Monoclonal(N2B2), IgG2a, kappa; Mouse, Monoclonal (1A7-2B7), IgG1; Mouse,Monoclonal (55B709.3), IgG and Mouse, Monoclonal B-S23*IgG1.

Examples of “Monoclonal antibodies for measuring CRP”, include withoutlimitation: Mouse, Monoclonal (108-2A2); Mouse, Monoclonal (108-7G41D2);Mouse, Monoclonal (12D-2C-36), IgG1; Mouse, Monoclonal (1G1), IgG1;Mouse, Monoclonal (5A9), IgG2a kappa; Mouse, Monoclonal (63F4), IgG1;Mouse, Monoclonal (67A1), IgG1; Mouse, Monoclonal (8B-5E), IgG1; Mouse,Monoclonal (B893M), IgG2b, lambda; Mouse, Monoclonal (C1), IgG2b; Mouse,Monoclonal (C11F2), IgG; Mouse, Monoclonal (C2), IgG1; Mouse, Monoclonal(C3), IgG1; Mouse, Monoclonal (C4), IgG; Mouse, Monoclonal (C5), IgG2a;Mouse, Monoclonal (C6), IgG2a; Mouse, Monoclonal (C7), IgG1; Mouse,Monoclonal (CRP103), IgG2b; Mouse, Monoclonal (CRP11), IgG1; Mouse,Monoclonal (CRP135), IgG1; Mouse, Monoclonal (CRP169), IgG2a; Mouse,Monoclonal (CRP30), IgG1; Mouse, Monoclonal (CRP36), IgG2a; Rabbit,Monoclonal (EPR283Y), IgG; Mouse, Monoclonal (KT39), IgG2b; Mouse,Monoclonal (N-a), IgG1; Mouse, Monoclonal (N1G1), IgG1; Monoclonal(P5A9AT); Mouse, Monoclonal (S5G1), IgG1; Mouse, Monoclonal (SB78c),IgG1; Mouse, Monoclonal (SB78d), IgG1 and Rabbit, Monoclonal (Y284),IgG.

Examples of “Monoclonal antibodies for measuring SAA”, include withoutlimitation: Mouse, Monoclonal (SAA15), IgG1; Mouse, Monoclonal (504),IgG2b; Mouse, Monoclonal (SAA6), IgG1; Mouse, Monoclonal (585), IgG2b;Mouse, Monoclonal (426), IgG2b; Mouse, Monoclonal (38), IgG2b; Mouse,Monoclonal (132), IgG3; Mouse, Monoclonal (S3-F11), IgM; Mouse,Monoclonal (513), IgG1; Mouse, Monoclonal (291), IgG2b; Mouse,Monoclonal (607), IgG1; Mouse, Monoclonal (115), IgG1; Mouse, Monoclonal(B332A), IgG1; Mouse, Monoclonal (B336A), IgG1; Mouse, Monoclonal(B333A), IgG1; Rabbit, Monoclonal (EPR2927); Rabbit, Monoclonal(EPR4134); Mouse, Monoclonal (Reu86-1), IgG1; Mouse, Monoclonal(Reu86-5), IgG1; Mouse, Monoclonal (291), IgG2b kappa; Mouse, Monoclonal(504), IgG2b kappa; Mouse, Monoclonal (585), IgG2b kappa; Mouse,Monoclonal (S3), IgM kappa; Mouse, Monoclonal (mcl), IgG2a kappa; Mouse,Monoclonal (Reu 86-2), IgG2a; Mouse, Monoclonal (3C11-2C1), IgG2b kappaand Rabbit, Monoclonal (EPR2926), IgG.

Polyclonal antibodies for measuring DETERMINANTS include withoutlimitation antibodies that were produced from sera by activeimmunization of one or more of the following: Rabbit, Goat, Sheep,Chicken, Duck, Guinea Pig, Mouse, Donkey, Camel, Rat and Horse.

Examples of Detection agents, include without limitation: scFv, dsFv,Fab, sVH, F(ab′)2, Cyclic peptides, Haptamers, A single-domain antibody,Fab fragments, Single-chain variable fragments, Affibody molecules,Affilins, Nanofitins, Anticalins, Avimers, DARPins, Kunitz domains,Fynomers and Monobody.

EXAMPLES EXAMPLE 1: General Methods

Clinical Study Overview

We performed a multi-center, observational, prospective clinical studywhose goal was to develop and test a DETERMINANT-signature for thepurpose of rapid and accurate diagnosis of patients with viral andbacterial diseases. We recruited a total of 655 patients of whom 609 hada suspected infectious disease and 46 had a non-infectious disease(control group). The study was approved by the institutional reviewboards (IRB) of Bnai Zion and Hillel Yaffe Medical Centers in Israel,where patients were recruited from 2010 to 2012.

An overview of study workflow is depicted in FIG. 1. Briefly, adata-minable electronic case report form (eCRF) was used to record theclinical investigations, medical history, microbiological, radiological,and laboratory data of each patient (eCRF records were designed topreserve patient anonymity). Based on the clinical syndrome, one or moreof the following samples were sent to thorough microbiological andmolecular investigations: blood, urine, stool, sputum, cerebrospinalfluid (CSF), and nasal swabs. A total of 44 different pathogen strainswere identified in the cohort of patients with suspected infectiousdiseases through the composite application of cultures, serology,antigen assays, and multiplex-PCRs methodologies.

Diagnosis (bacterial, viral, mixed, non-infectious, and undetermined)was determined by a panel of at least three experts (the attendingphysician at the hospital, two independent senior infectious diseaseexperts [IDEs], and a senior pediatrician if the patient was ≦18 yearsof age), based on a consensus or majority decision of the expert panel,and was recorded on the eCRF. In addition, we quantified the levels of570 different analyte biomarkers (e.g., proteins and metabolites) inblood drawn from these patients (some of the proteins were only measuredin a subset of the patients due to sample volume constraints). Weconstructed a database that included all the eCRF-contained data foreach patient (i.e., hundreds of numerical and categorical features aswell as the biomarker biochemical measurements). This database was thenused to develop and test the DETERMINANT-signatures.

Inclusion Criteria

Patients who were at least one month old and were willing (either thesubject or legal guardian) to sign an informed consent were eligible forinclusion. For the infectious and non-infectious disease groups,additional inclusion criteria had to be met. These included:

-   -   Infectious disease group:        -   Peak fever >37.5° C.        -   Clinical suspicion of an acute infectious disease        -   Symptoms duration ≦10 days    -   Non-infectious disease control group:        -   Clinical suspicion of a non-infectious disease            Exclusion Criteria

Patients who met the following criteria were excluded from the study:

-   -   Evidence of another episode of acute infectious disease in the        last two weeks    -   Diagnosed congenital immune deficiency (CID)    -   Current treatment with immunosuppressive therapy such as:        -   Active chemotherapy        -   Post-transplant drugs        -   High dose steroids (>40 mg/day prednisone or equivalent)        -   Active radiotherapy        -   Immune-modulating/suppressive drugs including monoclonal            antibodies, intravenous immunoglobulin (IVIG), cyclosporine,            and anti-tumor necrosis factor (TNF) agents    -   Current treatment with immunostimulants such as:        -   Interleukin (IL)-2        -   Granulocyte colony-stimulating factor (G-CSF) or            granulocyte-macrophage colony-stimulating factor (GM-CSF)        -   Interferon (all kinds)    -   An active hematological malignancy (e.g., chronic lymphocytic        leukemia [CLL])    -   A diagnosis of myelodysplastic syndrome (MDS) or        myeloproliferative disease (MPD)    -   A proven or suspected human immunodeficiency virus (HIV)-1,        hepatitis B virus (HBV), or hepatitis C virus (HCV) infection        The Enrollment Process

After signing an informed consent, each patient underwent the followingprocedures:

-   -   Physical examination and recording of baseline variables        including:        -   Demographics: gender, age, date of birth, date of            recruitment, site of recruitment, etc.        -   Medical history: main complaints, background diseases,            chronically-administrated drugs, time of symptom onset,            maximal fever, etc.        -   Physical examination: directed physical examination, pulse,            auscultation, throat exam, skin rash, lymphadenopathy            screening, etc.        -   Disease-specific variables (e.g., chest X-ray for suspected            lower respiratory tract infections [LRTI], flank tenderness            for suspected urinary tract infection [UTI])        -   Complete blood count (CBC) investigation including: whole            blood count, absolute neutrophil count (ANC), % neutrophils,            % lymphocytes, etc.    -   Chemistry lab: Creatinine, urea, liver enzymes, etc.    -   Sampling of the upper respiratory tract with a nasal swab for        further microbiological investigation    -   Sample collection based on clinical symptoms (e.g., urine        culture in a patient with a suspected UTI, stool sampling in a        patient with a suspected gastroenteritis)    -   Blood sampling for analyte biomarker measurements in MeMed labs:        2-6 ml of peripheral venous blood was collected in EDTA        containing CBC tubes. The blood was then stored in 4° degrees        for 1-4 hours.

Thirty days after enrollment, disease course and response to treatmentwere recorded on the eCRF as well as details such as clinical,radiological, laboratory, and microbiological results that wereunavailable at the day of enrollment.

Microbiological and Molecular Tests

To enable the expert panel to establish a final diagnosis with highconfidence level, we performed a thorough microbiological and molecularinvestigation by testing for most of the disease-causing agents in theWestern world. In this section, we present an overview of themicrobiological and molecular investigations.

For each patient, we applied two state-of-the-art CE-in vitrodiagnostics (IVD)-marked multiplex PCR assays on the specimens obtainedfrom the nasopharyngeal swab:

-   -   The Seeplex® RV15 ACE (SeeGene Ltd, Seoul, Korea). This assay is        designed to detect the majority of known respiratory viruses (15        virus subgroups including, parainfluenza virus 1, 2, 3, and 4,        coronavirus 229E/NL63, adenovirus A/B/C/D/E, bocavirus 1/2/3/4,        influenza virus A and B, metapneumovirus, coronavirus OC43,        rhinovirus A/B/C respiratory syncytial virus A and B, and        Enterovirus)    -   Seeplex® PneumoBacter ACE (SeeGene Ltd, Seoul, Korea). This        assay is designed to detect six pneumonia-causing bacteria        simultaneously (Streptococcus pneumoniae [SP], Haemophilus        influenza [HI], Chlamydophila pneumonia[CP], Legionella        pneumophila[LP], Bordetella pertussis[BP], and Mycoplasma        pneumonia [MP])

Patients were tested for additional pathogens according to theirsuspected clinical syndrome (for details see Clinical Study Protocol).For example:

-   -   Stool samples from patients with gastroenteritis were analyzed        using a multiplex PCR assay designed to detect 10 pathogens        (Rotavirus, Astrovirus, Enteric adenovirus, Norovirus GI,        Norovirus GII, Vibrio spp., Shigella spp., Campylobacter spp.,        Clostridium Difficile Toxin B, and Salmonella spp.)    -   Serological testing for cytomegalovirus (CMV), Epstein bar virus        (EBV), MP, and Coxiella Burnetii (Q-Fever) was performed in all        the clinically relevant subgroups    -   Blood, urine, and stool cultures were performed in clinically        relevant subgroups

Overall, our process detected a pathogen in >50% of the patients with aninfectious disease. We also used these results to examine the yield andaccuracy of different diagnostic methods and to evaluate the rates offalse discovery among patients with a non-infectious disease.

Creating the Reference Standard

Currently, no single reference standard exists for determining bacterialand viral infections in a wide range of clinical syndromes. Therefore,we followed the Standards for Reporting of Diagnostic Accuracy (STARD)recommendation (Bossuyt et al. 2003) and created a highly rigorouscomposite reference standard for testing the DETERMINANT signatures. Thecomposite reference standard was created in two steps. First, for eachpatient we performed a thorough investigation. This included thecollection of traditional types of diagnostic information such asrecording of medical history, clinical symptoms, disease course, and labmeasurements, as well as more advanced diagnostic information includingmicrobiological, serological, and molecular investigations (as describedabove). Then, we gave all the accumulated raw information to a panel ofat least three experts (for adult patients [>18 years of age], theexperts included the attending physician at the hospital and twoindependent senior IDEs; for children [≦18 years of age], the panelincluded a senior pediatrician as a fourth member of the expert panel).Based on the information, each member of the expert panel assigned oneof the following diagnostic labels to each of the patients: (i)bacterial; (ii) viral; (iii) mixed (i.e., bacterial and viralco-infection); (iv) non-infectious; or (v) undetermined. Importantly,the experts were blinded to the diagnostic labels of their peers on theexpert panel. The diagnosis was then determined by majority of theexpert panel. In our study, after applying the aforementioned process tothe enrolled patients (n=575), the cohort included 242 patients (42%)with a viral infection, 208 patients (36%) with a bacterial infection,34 patients (6%) with a mixed infection, 46 patients (8%) with anon-infectious disease, and 45 patients (8%) with an undetermineddiagnosis (either because no majority was reached by the expert panel[6% of all patients] or because the panel assigned the patient an‘undetermined’ diagnosis [2% of all patients]) (FIG. 2).

The diagnostic labels assigned by our expert panel were then used tocreate cohorts with an increasing level of confidence.

-   -   The majority cohort: Patients were included in this cohort if        they were assigned a diagnosis of a bacterial (‘bacterial        patient’), viral (‘viral patient’), mixed infection (‘mixed        patient’), or non-infectious disease, by a majority (>50%) of        the expert panel.    -   The consensus cohort: This subset of the majority cohort        included the patients for whom the expert panel assigned a        diagnosis (bacterial, viral, mixed, or non-infectious)        unanimously.    -   The clear diagnosis cohort: This subset of the consensus cohort        included patients with a bacterial or viral infection that were        assigned these diagnoses unanimously by the expert panel and who        also met the following additional criteria. To be included as a        bacterial patients, patients had to have bacteremia (with        positive blood culture), bacterial meningitis (with positive CSF        culture or >1,000 neutrophils/μL), pyelonephritis (with positive        urine culture and an ultrasound information of renal        involvement), UTI (with positive urine culture), septic shock        (with positive blood culture), cellulitis, or peri-tonsillar        abscess (proven by surgical exploration) (Thorn et al. 1977). To        be included as a viral patient, patients had to have a positive        microbiological isolate of an obligatory virus.

Of note, in the following examples tables and figures, unless explicitlymentioned otherwise, patient reference standards were determined basedon the majority cohort. The above-mentioned composite reference standardstrategy adheres to the recommended best practice guidelines in studiesof diagnostics of infectious disease. The DETERMINANT andDETERMINANT-signature performances reported herein were analyzed againstthis reference standard.

Measurements of membrane bound or intra-cellular polypeptideDETERMINANTS Whole blood was fractionated to cellular and plasmafractions and subsequantially treated with red blood cell lysing buffer(BD Bioscience). White blood cells were subsequently washed three timeswith phosphate buffered saline pH 7.3. In order to measure the levels ofmembrane associated DETERMINANT polypeptides, the cells were incubatedwith primary antibodies for 40 minutes, washed twice and incubated withPE conjugated secondary antibody (Jackson Laboratories, emission 575 nm)for additional 20 minutes. In case of intracellular DETERMINANTpolypeptides, cells were first fixed and permeabilized with fixation andpermeabilization buffer kit (eBioscience). Following fixation andpermeabilization cells were incubated with primary antibodies for 40minutes, washed twice and incubated with PE conjugated secondaryantibody for additional 20 minutes. IgG Isotype controls were used foreach mode of staining as negative control background. Following thestaining procedure, cells were analyzed by using an LSRII flowcytometer. Granulocytes, monocytes, platelets and lymphocytes weredistinguished from each other by using an SSC/FSC dot plot. Backgroundand specific staining were determined for lymphocytes, monocytes andgranulocytes for each specific antigen. Total leukocytes mean levels wascomputed by summing the DETERMINANT polypeptides levels of all the celltypes and dividing by the white blood count.

Polypeptide-DETERMINANTS that were measured using this protocol include:

CHP, CMPK2, CORO1C, EIF2AK2, ISG15, RPL22L1, RTN3, CD112, CD134, CD182,CD231, CD235A, CD335, CD337, CD45, CD49D, CD66A/C/D/E, CD73, CD84, EGFR,GPR162, HLA-A/B/C, ITGAM, NRG1, RAP1B, SELI, SPINT2, SSEA1, EIF4B,IFIT1, IFIT3, LOC26010, MBOAT2, MX1, OAS2, RSAD2, ADIPOR1, CD15, CD8A,IFITM1, IFITM3, IL7R, ARG1, ARPC2, ATP6V0B, BCA-1, BRI3BP, CCL19-MIP3b,CES1, CORO1A, HERC5, IFI6, IFIT3, KIAA0082, LIPT1, LRDD, MCP-2, PARP9,PTEN, QARS, RAB13, RPL34, SART3, TRIM22, UBE2N, XAF1 and ZBP1.

Measurements of Soluble-DETERMINANTS Using ELISA

To determine the concentrations of soluble-DETERMINANTS in human plasmasamples we used a standard Sandwich ELISA (Enzyme-linked immunosorbentassay). Briefly, the wells of 96-well plate were coated withcapture-antibody specific to the soluble DETERMINANT of interest anddiluted in coating buffer (e.g. 1×PBS) followed by overnight incubationat 4° C. The wells were washed twice with washing buffer (e.g. 1×PBSwith 0.2% Tween-20) and subsequently blocked with blocking buffercontaining proteins (e.g. 1×PBS with 0.2% Tween-20 and 5% non-fat milk)for at least 2 hours at room temperature or overnight at 4° C. This thatstep increases assay signal-to-noise-ratio. Wells were then washed twicewith washing buffer. Protein standard and plasma samples were dilutedusing a dilution buffer (e.g. 1×PBS with 0.2% Tween-20 and 5% non-fatmilk) at the adequate concentration and dilution factors, respectively,followed by a two hour incubation at room temperature. Then, the wellswere washed three times with the washing buffer and subsequentlyincubated with biotinylated detection-antibody specific to the solubleDETERMINANT of interest, diluted in blocking buffer for at least twohours at room temperature.

The wells were washed four times with a washing buffer and thenincubated with streptavidin-HRP (i.e. horseradish peroxidase) diluted inblocking buffer for one hour at room temperature. The wells were washedfour times with the washing buffer and then incubated with a reactionsolution that contained a chromogenic HRP substrate (e.g. TMB; 3, 3′,5,5′-Tetramethylbenzidine). After adequate color development, a stopsolution was added to each well. The absorbance of the HRP reactionproduct was determined with an ELISA plate reader. Soluble polypeptidesthat we measured using the above mentioned protocol comprise of: B2M,CHI3L1, Mac-2BP, SAA, TRAIL, sCD62L, sTREM, IL11, IL1RA, IP10, I-TAC andTNFR1.

Measurements of Soluble DETERMINANTS Using Luminex

To determine the concentrations of soluble DETERMINANTS in human plasmasamples we also used the xMAP immunoassay (Luminex Corporation, Austin,Tex.) (protocol details are available from the supplier). Briefly, theassay uses five-micron polystyrene beads that have been impregnated witha precise ratio of two fluorescent dyes, creating up to 100 spectrallyidentifiable beads. The surface of these beads is coated with carboxylterminals (an estimated one million per bead), which serve as theattachment point for the analyte specific antibody. Using standardimmunoassay principles, a sandwich format or competition assay wasperformed for each target biomarker. This included preparation ofstandards with predetermined analyte concentrations, six hour incubationof the sample followed by a flow cytometer readout.

Two lasers query the beads: one for its specific ID number; the secondfor the intensity of the phycoerythrin (PE) signal resulting from theimmunoassay. This assay enables the simultaneous measurement of a fewdozen analyte specific beads to be measured simultaneously thus enablingbiomarker screening.

More specifically, prepare standards and antibody conjugated beads andsamples within one hour of performing the assay. Reconstitute theprotein standard in 0.5 mL of Assay Diluent when working withserum/plasma samples, or 50% Assay Diluent+50% of serum matrix for othertypes of samples. Avoid mixing. Determine the number of wells requiredfor the assay. Standard curves and samples may be run singly or inreplicates, as desired. Pre-wet the 96 micro-titer plate. Pipette 0.2 mLof Working wash solution into designated wells. Wait 15 to 30 secondsand aspirate the wash solution from the wells using the vacuum manifold.Immediately before dispensing, vortex the beads for 30 seconds followedby sonication in a sonicating water bath for 30 seconds. Pipette 25 uLof the desired beads into each well. Once dispensed the beads should bekept protected from light using an aluminum foil-wrapped plate cover.Aspirate the liquid by gentle vacuum using the vacuum manifold. Preparea 1× capture bead solution from the additional 10× capture beadconcentrate(s) to be multiplexed. Pipette 25 uL of the additional 1×bead solution into each well. Add 0.2 mL Working wash solution into thewells. Allow the beads to soak for 15 to 30 seconds, then remove theWorking wash solution from the wells by aspiration with the vacuummanifold. Repeat this washing step. Blot the bottom of the filter plateon clean paper towels to remove residual liquid. Pipette 50 uLincubation buffer into each well.

To the wells designated for the standard curve, pipette 100 uL ofappropriate standard dilution.

To the wells designated for the sample measurement, pipette 50 uL assaydiluent followed by 50 uL sample. Incubate the plate for 2 hours at roomtemperature on an orbital shaker. Shaking should be sufficient to keepbeads suspended during the incubation (500-600 rpm). Ten to fifteenminutes prior to the end of this incubation, prepare the biotinylateddetector antibody. After the 2 hour capture bead incubation, remove theliquid from the wells by aspiration with the vacuum manifold. Add 0.2 mLWorking wash solution to the wells. Allow the beads to soak for 15 to 20seconds, then aspirate with the vacuum manifold. Repeat this washingstep. Blot the bottom of the filter plate on clean paper towels toremove residual liquid. Add 100 uL of prepared 1× Biotinylated DetectorAntibody to each well and incubate the plate for 1 hour at roomtemperature on an orbital shaker. Shaking should be sufficient to keepbeads suspended during incubation (500-600 rpm). Ten to fifteen minutesprior to the end of the detector incubation step, prepare theStreptavidin-RPE. Remove the liquid from the wells by aspiration withthe vacuum manifold. Add 0.2 mL Working wash solution to the wells.Allow the beads to soak for 15 to 30 seconds, then aspirate with thevacuum manifold. Repeat this washing step. Blot the bottom of the filterplate with clean paper towels to remove residual liquid. Add 100 uL ofthe prepared 1× Streptavidin-RPE to each well and incubate the plate for30 minutes at room temperature on an orbital shaker. Shaking should besufficient to keep beads suspended during incubation (500-600 rpm).Prepare the Luminex instrument during this incubation step. Remove theliquid from the wells by aspiration with the vacuum manifold. Note thata minimal pressure of 5 inches Hg is required. Wash the beads by adding0.2 mL working wash solution to the wells, allow the beads to soak for10 seconds, then aspirate with the vacuum manifold. Repeat this washingstep two additional times for a total of 3 washes. Add 100 uL workingwash solution to each well. Shake the plate on an orbital shaker(500-600 rpm) for 2-3 minutes to re-suspend the beads. Uncover theplate; insert plate into the XY platform of the Luminex instrument andanalyze the samples.

Determine the concentration of the samples from the standard curve usingcurve fitting software. The four parameter algorithm usually providesthe best fit. If the plates cannot be read on the day of the assay, theymay be covered and stored in a dark location overnight at 2-8° C. forreading the following day without significant loss of fluorescentintensity. Aspirate working wash solution from stored plated and add 100uL fresh working wash solution. Place the plates on an orbital shakerfor 2-3 minutes prior to analysis. Soluble polypeptides that we measuredusing the above mentioned protocol comprise of: BCA-1, TRAIL, Eotaxin,IL1a, IP10, MCP and VEGFR2.

Measurements of CRP Soluble DETERMINANT

CRP concentrations were measured using automated immunoassay machines inthe chemical laboratories of the hospitals in which the patients wereenrolled.

DETERMINANT Normalization

To avoid numerical biases, some multi parametric models (such as SVMs)require that the numerical DETERMINANTS used in the model be similarlyscaled. Thus, when performing multi-parametric analysis, we used thefollowing linear normalization: the DETERMINANT levels of each patientwere divided by the DETERMINANT mean levels computed over all thepopulation in the study. To avoid numerical errors due to outliers(>mean±3×std), such measurements were truncated and assigned the valuemean±3×std.

Handling of Missing Values/Censoring/Discontinuations

Missing DETERMINANT values might arise due to technical issues in themeasurement process (e.g. deterioration of an antibody used to measure aspecific DETERMINANT). Furthermore, some of the DETERMINANTS, especiallythe polypeptide DETERMINANTS, could only be measured on a subset of thepatients, because the amount clinical sample drawn from any givenpatient was insufficient in order to measure the entire panel ofDETERMINANTS. Consequentially, some subjects may have missing values forsome of their DETERMINANT measurements. To address this, the accuracy ofeach DETERMINANT or multi-DETERMINANT signature is computed only on thepatients that do not have any missing value in the respective signature.

DETERMINANT Diagnosis Statistical Analysis

The classification accuracy and statistical significant of individualDETERMINANTS was measured in terms of sensitivity, specificity, PPV,NPV, MCC, AUC and Wilcoxon rank sum P-value or t-test P-value. Thediagnostic accuracy of the multi-DETERMINANT signatures was determinedusing a leave-10%-out cross-validation scheme for training and testing asupport vector machine (SVM) with a linear (CJC Burges, 1998).Classification accuracy was measured using the same criteria as in thesingle DETERMINANT. We also tested the classification accuracy usingother multi-parametric models including: (i) an RBF kernel SVM, (ii) anartificial neural network (one hidden layer with three nodes, one outputnode and tansig transfer functions), (iii) a naïve bayes network and(iv) a k-nearest-neighbor classification algorithm. For most of thetested DETERMINANT combinations the linear SVM yielded roughly the sameclassification results in terms of AUC and MCC compared the othermodels. We therefore report herein only the results of the linear SVM.

EXAMPLE 2: To Facilitate a Diagnostic Solution that is BroadlyApplicable we Performed a Clinical Study on a Highly HeterogeneousCohort of Patients

Summary of the Patient Cohorts Used in this Study

A total of 655 patients were recruited for this study and 575 patientswere eligible for enrollment. Based on the reference standard processdescribed above, patients were assigned to five different diagnosisgroups: viral infection (42% of patients), bacterial infection (36% ofpatients), mixed infection (6% of patients), non-infectious disease (8%of patients), and undetermined (8% of patients) (FIG. 2). In total, 92%of all enrolled patients were assigned a diagnosis, a rate whichapproaches the literature-documented limit (Clements et al. 2000;Johnstone et al. 2008; Hatipo{hacek over (g)}lu et al. 2011).

The development and testing of the DETERMINANT signature technology wasperformed in a series of patient cohorts with increased confidencelevels, as described above (Creating the reference standard). Of the 575enrolled patients, 530 had a diagnosis (bacterial, viral, mixed, ornon-infectious) assigned by the majority of the expert panel. Of these530 patients, 376 had these diagnoses assigned unanimously (i.e., a‘consensus’ diagnosis). Of the 376 patients, 170 patients had a cleardiagnosis determined as described above.

The various cohorts and the number of bacterial, viral, mixed, andnon-infectious patients within each cohort are depicted in FIG. 3.

Age and Gender Distribution

Patients of all ages were recruited to the study. The study population(n=575) included more pediatric (≦18 years) than adult (>18 years)patients (60% vs 40%). The age distribution was relatively uniform forpatients aged 20-80 years and peaked at ≦4 years of age for pediatricpatients (FIG. 4). The observed age distribution for pediatric patientsis consistent with that expected and represents the backgrounddistribution in the inpatient setting (Craig et al. 2010) (e.g., theemergency department [ED], pediatrics departments, and internaldepartments).

Patients of both genders were recruited to the study. The patientpopulation was balanced in respect to gender distribution (49% females,51% males).

Isolated Pathogens

We used a wide panel of microbiological tools in order to maximizepathogen isolation rate. At least one pathogen was isolated in 53% ofpatients with an acute infectious disease (49% of all 575 enrolledpatients). A total of 33 different pathogens were actively detectedusing multiplex PCR, antigen detection, and serological investigation.Additional 11 pathogens were isolated using standard culture techniquesor in-house PCR. Altogether, 44 different pathogens from all majorpathogenic subgroups were isolated (FIG. 5A). This rate of pathogenidentification is similar to that reported in previously publishedstudies (Cillóniz et al. 2011; Restrepo et al. 2008; Song et al. 2008;Johansson et al. 2010; Shibli et al. 2010) and included pathogens fromall major pathogenic subgroups (Gram-negative bacteria, Gram-positivebacteria, atypical bacteria, RNA viruses, and DNA viruses). In nearly20% of the patients, pathogens from >1 of the aforementioned pathogenicsubgroups were detected (FIG. 5A).

The pathogenic strains found in this study are responsible for the vastmajority of acute infectious diseases in the Western world and includedkey pathogens such as Influenza A/B, respiratory syncytial virus (RSV),Parainfluenza, E. Coli, Group A Streptococcus, etc.

Notably, analysis of the isolated pathogens revealed that none of thepathogens is dominant (FIG. 5B). The absence of influenza A or RSVdominance is attributed to two reasons: year-round sampling (i.e.,sampling was not limited to the winter season) and the non-occurrence ofinfluenza and RSV epidemics in Israel during the study timeframe(2010-2012).

Involved Physiologic Systems and Clinical Syndromes

The infectious disease patients (all patients with a final diagnosisexcluding those with non-infectious diseases, n=484) presented withinfections in a variety of physiologic systems (FIG. 6). The mostfrequently involved physiologic system was the respiratory system (45%),followed by systemic infections (18%). All infections that did notinvolve the aforementioned systems and were not gastrointestinal,urinary, cardiovascular, or central nervous system (CNS) infections werecategorized as ‘Other’ (e.g., cellulitis, abscess). The observeddistribution of physiologic system involvement represents the naturaldistribution and is consistent with that reported for large cohorts ofpatients sampled year-round (CDC.gov 2012).

The patients in our study (all enrolled patients, n=575) presented witha variety of clinical syndromes (FIG. 7) that reflects the expectedclinical heterogeneity in a cohort of pediatric and adult patientscollected year-round. The most frequent clinical syndrome was LRTI (25%)including mainly pneumonia, bronchitis, bronchiolitis, chronicobstructive pulmonary disease (COPD) exacerbation, and non-specificLRTI. The second most frequent clinical syndrome was URTI (20%)including mainly acute tonsillitis, acute pharyngitis, non-specificURTI, acute sinusitis, and acute otitis media. The third most frequentsyndrome was systemic infection (17%) including mainly fever without asource and occult bacteremia cases. Systemic infections were primarilydetected in children <3 years of age but were also detected in a fewadult patients.

Systemic infections constitute a real clinical challenge as balancingbetween patient risk and the costs of testing/treatment is unclear. Thenext most frequent syndromes were gastroenteritis (11%), UTI (8%), andcellulitis (4%). CNS infections (2%) included septic and asepticmeningitis. All other clinical syndromes (3%) were classified as ‘Other’and included less common infections (e.g., peritonsillar abscess, otitisexterna, epididymitis, etc.). The observed pattern of clinical syndromedistribution represents most of the frequent and clinically relevantsyndromes and is consistent with previously published large studies(Craig et al. 2010).

Core Body Temperature

Core body temperature is an important parameter in evaluating infectiousdisease severity. We examined the distribution of maximal bodytemperatures in all enrolled patients (n=575) using the highest measuredbody temperature (per-os or per-rectum). The distribution of the maximalbody temperatures was relatively uniform between 38° C. and 40° C. witha peak of at 39° C. (FIG. 8). Body temperature ≦37.5° C. was reportedfor 8% of patients (the subgroup of patients with non-infectiousdiseases). Body temperature ≧40.5° C. was rare (<3% of patients).

Altogether, the observed distribution represents the normal range oftemperatures in the clinical setting (Craig et al. 2010).

Time from Symptoms Onset

‘Time from symptoms’ was defined as the duration (days) from theappearance of the first presenting symptom (the first presenting symptomcould be fever but could also be another symptom such as nausea orheadache preceding the fever). The distribution of ‘time from symptoms’in our cohort (all enrolled patients, n=575) peaked at 2-4 days afterthe initiation of symptoms (40% of patients) with substantialproportions of patients turning to medical assistance either sooner orlater (FIG. 9). The observed distribution of time from initiation ofsymptoms represents a typical pattern in the clinical setting.

Comorbidities and Chronic Drug Regimens

Comorbidities and chronic drug regimens may, theoretically, affect adiagnostic test. Our patient population (all enrolled patients, n=575)included patients (70%) that had no comorbidities and were not treatedwith chronic medications and patients (30%) that had ≧1 chronic diseaseand were treated with chronic medications. The most frequent chronicdiseases in our patient population were hypertension, lipidabnormalities, lung diseases (e.g., COPD, asthma, etc.) diabetesmellitus (mostly type 2), and ischemic heart disease, mirroring the mostcommon chronic diseases in the Western world (FIG. 10A). All patientswith chronic diseases were chronically treated with medications. Thedistribution of chronic drugs used by our patient population stronglycorrelated with the range of reported chronic diseases (e.g., 42% of thepatients with comorbidities had lipid abnormalities and lipid loweringagents were the most frequently used drugs). Other frequently used drugsincluded aspirin, blood glucose control drugs, and beta blockers (FIG.10B).

Patient Recruitment Sites

The recruitment sites in our study included ED (pediatric, adults) andother hospital departments (pediatric, adults). The pediatric ED was themost common recruitment site (43%) and the other sites were comparable(17-22%) reflecting a relatively balanced recruitment process. The ratiobetween ED patients and hospitalized patients was ˜1:1 for adults and˜2:1 for children (FIG. 11).

Comparing Baseline Characteristics of the Bacterial and Viral Groups

We compared baseline characteristics of the bacterial and viral groupsby age (children vs adults; Table 4). In both children and adults, labparameters such as WBC levels, neutrophils (%), lymphocytes (%) and ANC,differed significantly (P<0.001) between bacterial and viral patients,in accordance with the well-established differences between these twoinfection types (Christensen, Bradley, and Rothstein 1981; Peltola,Mertsola, and Ruuskanen 2006). In children, significant differences werealso observed for age (P<0.001) and maximal body temperature (P<0.007).These findings are consistent with the increased prevalence of viralinfections in younger children and with the higher temperature oftenpresent in bacterial vs. viral infections (Pickering and DuPont 1986).The other variables (e.g., respiratory rate, urea, and heart rate) didnot demonstrate a statistically significant difference between thebacterial and viral groups indicating a similar clinical appearance inboth groups.

Characteristics of Excluded Patients

Of the 655 patients recruited for the study, 80 patients (12%) wereexcluded. The most frequent reason for exclusion was having a feverbelow the study threshold of 37.5° C. (n=40; 50% of all excludedpatients), followed by time from symptom initiation of >10 days (n=15,19% of all excluded patients) and having a recent (in the preceding 14days) infectious disease (n=13, 16% of all excluded patients). Otherreasons for exclusion included having a malignancy (hematological [9% ofall excluded patients], solid [5% of all excluded patients]) and beingimmunocompromised (e.g., due to treatment with an immunosuppressivedrug; 1% of all excluded patients).

EXAMPLE 3: Measurements of DETERMINANT Levels were Highly ReproducibleAcross Day-to-Day Technical Repeats and Different Measurement Platforms

Assay Performance and QA

Calibration curves were linear within the physiological concentrationrange Standard preparations provided by the assay manufacturer served asa reference standard for the calibration curves. Representative samplesof calibration curves for TRAIL, Mac-2BP and SAA are presented in FIG.12. We found that all the optimal cutoff values between bacterial andviral infections were in the linear range of the scale and that allstandard curves exhibited a dynamic range of ˜2-2.5 log scale.

Intra-Assay Variability

We tested the intra-assay variability on eight independent serum samplesof patients within the same ELISA plate (FIG. 13). We found intra-assayCV % of 4.4%, 7.5% and 4.4% for TRAIL, Mac-2-BP, and SAA respectively.These values are within the range of normal intra-assay variationcompared with other manual ELISA assays. Using automated devices orimproving assay format may lower the intra-assay variability andincrease biomarker accuracy.

Inter-Assay Variability

We tested the inter-assay variability for TRAIL, Mac-2BP, and SAA in 20,8 and 8 independent samples, respectively. We observed variations of6.6%, 8.1%, and 12.3%, respectively (FIG. 14).

Analyte Levels were Similar in Serum and Plasma

We tested the levels of TRAIL, Mac-2-BP, and SAA in a cohort of pairedserum and plasma samples of 32, 35 and 46 individuals, respectively. Forall three analytes we observed a strong correlation (r2 between 0.88 and0.98) and comparable concentrations (slopes between 0.92 and 1.05)between plasma and serum concentrations (FIG. 15).

Analytes are Stable Under Conditions Typical for the Clinical Setting

The utility of a biomarker depends on its stability in real-lifeclinical settings (e.g., its decay rate when the sample is stored atroom temperature prior to analyte measurement). To address this, weexamined the stability of TRAIL, Mac-2-BP, and SAA in serum samples fromfour, three, and five independent individuals during 21 hours at 4° C.(refrigeration) and 25° C. (room temperature). Aliquots of 100 μL fromeach plasma sample were pipetted into 0.2 mL tubes and kept at 4° C. or25° C. from 0 to 21 hours. Subsequently, we measured the levels of theanalytes (different time-points of the same analytes were measured usingthe same plate and reagents). The mean levels of all three analytes wereroughly stable over the first 21 hours at 4° C. The analyte half-livesat 25° C. were 24±5, >48, and >48 hours for TRAIL, Mac-2-BP, and SAA,respectively (FIG. 16). These half-lives are comparable to thoseobserved for other biomarkers used in the clinical emergency setting(Rehak and Chiang 1988; Boyanton and Blick 2002; Guder et al. 2007). Ofnote, in the real clinical setting, if the samples are stored at roomtemperature, the concentrations of TRAIL should be measured within about24 after the sample is obtained. Alternatively, the sample should bestored at lower than 12° C., and then TRAIL can be measured more than 24after obtaining the sample.

Measurements are Reproducible Across Different Platforms

The levels of TRAIL in 80 independent samples were tested using twodifferent platforms (ELISA and Luminex) and the results were correlatedand comparable (r2=0.89, P<10-5; FIG. 17). Importantly, the ELISA andLuminex assays differ in some basic aspects. For example, the Luminexassay is based on direct fluorescence detection, whereas ELISA is basedon colorimetric detection. Furthermore, the set of capture and detectionantibodies were different between the assays. Despite these and otherdifferences, the results were comparable demonstrating adoptability ofthe DETERMINANT-signature approach to other platforms.

EXAMPLE 4: Most Polypeptide-DETERMINANTS, Even Those with anImmunological Role, were not Differentially Expressed in Patients withDifferent Types of Infections

To screen for potential DETERMINANTS that might be differentiallyexpressed in different types of infections we performed biochemicalmeasurements of over 500 polypeptides, in samples taken from thepatients enrolled in the clinical study. We found that most DETERMINANTSwere not differentially expressed in subjects with different types ofinfections. Moreover, we found that even polypeptide-DETERMINATS thathave a well-established mechanistic role in the immune defense againstinfections or participate in inflammatory processes often showed poordiagnostic accuracy for identifying the source of infection. This pointis illustrated in FIG. 18 and Table 1, which show examples ofpolypeptide-DETERMINANTS with an established immunological orinflammatory role that were not differentially expressed betweenpatients with viral or bacterial infections. For example, differenttypes of INF-alpha (INF-a) have a well-established role in antiviralcellular processes. They are mainly produced by leukocytes and may bepotentiated by febrile temperatures. We measured the plasma levels ofINF-a in 22 bacterial and 27 viral patients and found no differentialresponse (Wilcoxon rank sum P=0.8) (FIG. 18). The protein INF-gamma(ING-g) is another cytokine that is critical to the innate and adaptiveimmunity against viral and bacterial infections, which showed nodifferential response (Wilcoxon rank sum P=0.9). TNF-alpha (TNF-a) is acytokine produced mainly by activated macrophages. It is a majorextrinsic mediator of apoptosis and was found to play a role in viralinfections (Gong et al. 1991). Following these observations hypothesizethat TNF-a may be used to diagnose the source of infection. We measuredTNF-a levels in patients with bacterial and viral infected patients andfound poor differential response (Wilcoxon rank sum P=0.9). Yet, anotherexample is CD95, a Fas ligand receptor that participates in the processof death-inducing-signaling-complex, during apoptosis. This receptor wasfound to be involved in the host response to different infections(Grassmé et al. 2000). We find that the levels of CD95 on lymphocytesand monocytes were not differentially expressed between bacterial andviral patients in a statistically significant manner (P=0.1, and P=0.9,respectively). We also measured the levels of many other interleukins,cytokines and their receptors, chemokines and their receptors, HLAs andother determinants that participate in the immune response to infectionand found that in most cases the levels of the determinants was notdifferentially expressed between viral and bacterial infections (formore examples see FIG. 18). Thus, an immunological or inflammatory roleof a polypeptide-DETERMINAT does not necessarily imply diagnosticutility.

EXAMPLE 5: In-Vitro Differential Response to Different Types ofInfections does not Necessarily Indicate a Corresponding In-VivoDifferential Response

We examined whether biomarkers that are differentially expressed duringin-vitro infections are also likely to be accurate diagnostic markersin-vivo. We found that in many cases, an in-vitro differentialexpression did not necessarily translate into the corresponding in-vivodifferential expression. The following section presents examples of thiscomparison.

Previous in-vitro studies indicated that the mRNA and protein levels ofarginase 1 (ARG1) are up regulated in viral infections and remain low inbacterial infections. Briefly, the in-vitro transfection of humanhepatoblastoma HepG2 cells and human hepatoma Huh-7 cells with aninfectious cDNA clone of Hepatitis C virus (HCV) resulted in aboutthreefold elevation of ARG1 mRNA and protein levels (P<0.01)(Cao et al.2009). In contrast, ARG1 mRNA expression levels of mouse macrophages,cocultured with H. pylori SS1, were not elevated (Gobert et al. 2002).

Taken together, these two in-vitro studies prompted us to examinewhether ARG1 may serve as a reliable in-vivo diagnostic marker that isup-regulated in viral infections while maintaining basal levels inbacterial infections. We measured the ARG1 protein levels of 41 patientswith bacterial infections and compared it to the levels in 46 patientswith viral infections. Measurements were performed on the granulocytes,lymphocytes and total leukocytes. In all cases, we did not observe anincrease of ARG1 levels in viral compared to bacterial infected patients(FIG. 19). Specifically, ARG1 levels on granulocytes were notdifferentially expressed (Wilcoxon rank sum P=0.3), whereas lymphocytesand total leukocytes showed a slight increase in bacterial compared toviral infected patients (Wilcoxon rank sum P=0.09, and 0.003respectively), an opposite behavior to the one reported in the in-vitrostudies.

Another example is interleukin-8 (IL-8), whose levels increased in cellculture medium of human gastric SGC-7901 adenocarcinoma cells aftertreatment with Helicobacter pyloriSydney strain 1 lipopolysaccharide(Zhou et al. 2008). In contrast, in-vivo IL-8 serum levels of H.pylori-infected patients were found similar to IL-8 serum levels of H.pylori-negative control group (Bayraktaroglu et al. 2004).

Thus, differential expression in different in-vitro infections does notnecessarily imply differential expression in-vivo.

EXAMPLE 6: DETERMINANTS that Differentiate Between Different Types ofInfections

We measured over 570 polypeptides and found that most (over 95%) did notdifferentiate between different types of infections. Diverging from thisnorm were unique subsets of polypeptides that showed consistent androbust differential response across a wide range of patientcharacteristics and pathogens (for details see patient characteristicssection). The following sections describe polypeptides and theircombinations, which were useful for diagnosing different sources ofinfection.

DETERMINANTS that Differentiate Between Bacterial Versus Viral InfectedSubjects

We identified a subset of DETERMINANTS that were differentiallyexpressed in subjects with bacterial versus viral infections in astatistically significant manner (Wilcoxon ranksum P<0.001). DETERMINANTnames and classification accuracies are listed in Table 2A. Thedistributions and individual subject measurements for each of theDETERMINANTS are depicted in FIG. 20 (dots corresponds to DETERMINANTSmeasurement in individual subjects and bars indicate group medians).Each subplot corresponds to a different DETERMINANT. The abbreviationsmono, lymp, gran, mean and total are used to denotepolypeptide-DETERMINANT measurements on monocytes, lymphocytes,granulocytes as well as mean and total leukocytes measurementsrespectively. The abbreviations intra and membrane are used to denoteproteins that were measured in the intra cellular and membrane fractionrespectively.

Additionally, we found that using non-specific mouse IgG1 and IgG3isotype controls as a primary antibody (coupled with the appropriatefluorescent marker) consistently showed an increased signal in thelymphocytes and monocytes of viral patients compared to bacterialpatients (Table 2A). A similar differential response was observed whenmeasuring the signal of PE conjugated goat IgG (Table 2A). Although thedifferential signal was weak in terms of absolute levels, compared tothe signal obtained from specific bindings, it was statisticallysignificant (Wilcoxon ranksum P<0.001). This phenomenon may be due tonon-specific binding of IgG to Fc gamma receptors, or other receptorsthat bind Ig like domains, whose levels may be elevated on host cellsthat respond to a viral infection.

DETERMINANTS that Differentiate Between Mixed Versus Viral InfectedSubjects

Differentiating between a mixed infection (i.e. bacterial and viralco-infection) and a pure viral infection is important for deciding theappropriate treatment. To address this we identified a set ofDETERMINANTS that were differentially expressed in subjects with mixedinfections versus viral infections in a statistically significant manner(Wilcoxon ranksum P<0.001). DETERMINANT names and classificationaccuracies are listed in Table 2B. The distributions and individualsubject measurements for each of the DETERMINANTS are depicted in FIG.21.

DETERMINANTS that Differentiate Between Mixed Versus Bacterial InfectedSubjects.

We identified a set of DETERMINANTS that were differentially expressedin subjects with mixed infections versus bacterial infections in astatistically significant manner (Wilcoxon ranksum P<0.001). DETERMINANTnames and classification accuracies are listed in Table 2C.

DETERMINANTS that Differentiate Between Bacterial or Mixed Versus ViralInfected Subjects.

We identified a set of DETERMINANTS that were differentially expressedin subjects with bacterial or mixed infections versus viral infectionsin a statistically significant manner (Wilcoxon ranksum P<0.001).DETERMINANT names and classification accuracies are listed in Tables 2D,2E and 2F.

DETERMINANTS that Differentiate Between Subjects with an InfectiousVersus a Non-Infectious Disease

We identified a set of DETERMINANTS that were differentially expressedin subjects with an infectious disease versus subjects with anon-infections disease in a statistically significant manner (Wilcoxonranksum P<0.001). DETERMINANT names and classification accuracy arelisted in Table 2G. The distributions and individual subjectmeasurements for some of the DETERMINANTS are depicted in FIG. 21B. Notethat the diagnostic accuracy reported in Table 2G was obtained despitethe presence of non-pathogenic micro-organisms in the group of patientswith a non-infectious disease (for details see FIG. 22). The presence ofsuch non-pathogenic micro-organisms poses a major challenge todiagnostic methods that seek to identify the pathogen directly, oftenleading to “false positives”. This challenge is overcome by some methodsof the present invention. To further establish the results someDETERMINANTS were measured on additional non-infectious patients (up to83 patients) as depicted in Table 2G.

DETERMINANTS that Differentiate Between Subjects with an InfectiousDisease Versus Healthy Subjects

We identified a set of DETERMINANTS that were differentially expressedin subjects with an infectious disease versus healthy subjects in astatistically significant manner (Wilcoxon ranksum P<0.001). DETERMINANTnames and classification accuracies are listed in Table 2H. Thedistributions and individual subject measurements for some of theDETERMINANTS are depicted in FIG. 21C. Note that the diagnostic accuracyreported in Table 2H was obtained despite the presence of non-pathogenicmicro-organisms in the healthy subjects (see FIG. 22). The presence ofsuch non-pathogenic micro-organisms in healthy subjects poses a majorchallenge to diagnostic methods that seek to identify the pathogendirectly, often leading to “false positives”. This challenge is overcomeby methods of the present invention.

EXAMPLE 7: DETERMINANT Signatures can Improve the Diagnostic Accuracy ofDifferent Infections Types

DETERMINANT Signatures for Differentiating Between Bacterial VersusViral Infected Subjects

We scanned the space of DETERMINANT combinations and identified pairsand triplets of DETERMINANTS whose combined signature (usingmulti-parametric models) differentiated between subjects with bacterialversus viral infections in a way that significantly improved over theclassification accuracy of the corresponding individual DETERMINANTS.

For example the diagnostic accuracy of TRAIL, Mac-2BP and CRP are 0.86,0.78 and 0.85 AUC respectively. The combination (TRAIL, CRP), (Mac-2B,CRP) and (TRAIL, Mac-2BP, CRP) show increased diagnostic accuracy of0.945, 0.939 and 0.954 AUC, respectively. Further examples of thecombined classification accuracies of DETERMINANT pairs, triplets andquadruplets are depicted in Table 3A, B, G and FIG. 23.

DETERMINANT Signatures for Differentiating Between Mixed Versus ViralInfected Subjects

We identified pairs of DETERMINANTS whose combined signaturedifferentiated between subjects with mixed versus viral infections. Thecombined classification accuracies of DETERMINANT pairs, triplets andquadruplets are depicted in Table 3C, D, G and FIG. 24.

DETERMINANT Signatures for Differentiating Between Subjects with anInfectious Disease Versus Subjects with a Non-Infectious Disease

We identified pairs of DETERMINANTS whose combined signaturedifferentiated between subjects with an infectious verses anon-infectious disease. The combined classification accuracies ofDETERMINANT pairs and triplets are depicted in Table 3E,F.

EXAMPLE 8: Performance Analysis: Mutli-DETERMINAT Signatures AccuratelyDiagnoses Different Sources of Infection

DETERMINANT Signatures that Include Measurements of CRP and TRAIL areHighly Accurate for Differentiating Between Patients with DifferentTypes of Infections

We find that DETERMINANT signatures that include TRAIL and CRP generateparticularly high levels of accuracy. By way of example and notlimitation, some the following sections present results we obtained forthe multi-DETERMINANT signature that combines the measurements of serumor plasma levels of TRAIL, CRP and Mac-2BP, termed “TCM-signature”.Examples of other multi-DETERMINANT signatures that produce accuratediagnosis include without limitation (TRAIL and CRP), (TRAIL, CRP andAge), (TRAIL, CRP and SAA), (TRAIL, CRP, SAA and IL1RA) and (TRAIL, CRP,SAA and IP10). By way of example, we assessed the diagnostic accuracy ofTCM-signature in a series of analyses using the aforementioned patientcohorts, starting with the cohort for which the confidence of thereference standard was the greatest. The cohort used in the firstanalysis included patients whose diagnosis (bacterial, viral) was clear(i.e., the ‘Clear [bacterial, viral]’ cohort). This cohort included 170patients. The cohorts used in the second and third analyses includedpatients who were diagnosed as either bacterial or viral patientsunanimously (the ‘Consensus [bacterial, viral]’ cohort; n=343), or bymajority (the ‘Majority [bacterial, viral]’ cohort; n=450) of the expertpanel. The fourth analysis evaluated the ability of TCM-signature todifferentiate viral from mixed infections in a cohort of patients whosediagnosis (either viral or mixed) was assigned by the majority of ourexpert panel (the ‘Majority [viral, mixed]’ cohort; n=276). The lastanalyses in this series evaluated whether the TCM-signature technologycould perform an accurate diagnosis even after adding back the patientswho were initially excluded from the study but for whom a viral orbacterial diagnosis was made by the expert panel (either unanimously orby majority).

The cohorts used for these analyses included 368 patients (unanimouslydiagnosed by the expert panel) and 504 patients (majority diagnosis).

Accuracy of Distinguishing Between Bacterial Vs Viral Infections inPatients Whose Diagnosis was Clear

We began by examining the accuracy of TCM-signature in bacterial andviral patients with a clear diagnosis (the ‘Clear [bacterial, viral]’cohort; for details see previous sections). Briefly, patients wereassigned a bacterial diagnosis if they were diagnosed unanimously by ourexpert panel and had bacteremia (with positive blood culture), bacterialmeningitis, pyelonephritis, UTI, septic shock, cellulitis, orperitonsillar abscess. Patients were assigned a viral diagnosis if theywere diagnosed unanimously by our expert panel and had a positivemicrobiological test for an obligatory virus. The cohort for thisanalysis included 170 patients (57 bacterial and 113 viral).

We tested the accuracy of the TCM-signature using a leave-10%-outcross-validation scheme and found a high diagnostic accuracy (AUC of0.96). Details of different diagnostic measures of accuracy and their95% CIs are depicted in FIG. 25 and Table 5.

The accuracy of the TCM-signature was also evaluated using a train setconsisting of ⅔ of the patients and an independent test set consistingof the remaining ⅓ of the patients. This evaluation yielded similarresults to those obtained using the cross validation scheme.

Accuracy of Distinguishing Between Bacterial Vs Viral Infections inPatients Whose Diagnosis was Determined by a Consensus of Experts

Next, we examined the accuracy of the TCM-signature in a cohort of 343patients who were unanimously diagnosed as bacterial (153 patients) orviral (190 patients) by our expert panel (the ‘Consensus [bacterial,viral]’ cohort). A leave-10%-out cross-validation scheme yielded a veryaccurate diagnosis with an AUC of 0.97. Additional measures ofdiagnostic accuracy and their 95% CIs are depicted in FIG. 26 and Table6. Assessment of the performance of the TCM-signature using a train set(⅔ of the patients) and an independent test set (⅓ of the patients),yielded similar results.

Since the pathogen repertoire found in children and adults oftendiffers, we stratified the patients by age and repeated the analysis. Wefound that the TCM-signature performance remained stable acrossdifferent age groups (FIG. 26).

Accuracy of Distinguishing Between Bacterial Vs Viral Infections inPatients Whose Diagnosis was Determined by Majority of the Expert Panel

Next, we examined the accuracy of the TCM-signature in a cohort ofpatients who were diagnosed as bacterial or viral by the majority of ourexpert panel (the ‘Majority [bacterial, viral]’ cohort). The cohortconsisted of 450 patients (208 bacterial, 242 viral). A leave-10%-outcross-validation scheme yielded a diagnosis with an AUC of 0.95.Additional measures of diagnostic accuracy and their 95% CIs aredepicted in FIG. 27 and Table 7. Assessment of the performance of theTCM-signature using a train set (⅔ of patients) and an independent testset (⅓ of patients), yielded similar results. Age-based stratificationanalysis also produced comparable results (FIG. 27 and Table 7).

The slight decrease in performance in this cohort compared with the‘Consensus (bacterial, viral)’ cohort (AUC of 0.95 vs 0.97) may bepartially attributed to the higher confidence in the diagnosis ofpatients in the latter cohort. Thus, the accuracy measures reported forthe ‘Majority (bacterial, viral)’ cohort probably represents a lowerbound on the true accuracy of the TCM-signature. Consequently, togenerate a conservative estimate of the TCM-signature performance, wereport on the ‘Majority’ cohorts from here onward, unless otherwisementioned.

Accuracy of Distinguishing Between Mixed Co-Infections Vs Pure ViralInfections

A total of 34 patients (˜6% of all patients with an infectious disease)were diagnosed by the majority of experts in our panel as having a mixedco-infection (i.e., a bacterial infection with a viral co-infection inthe background). Clinically, it is important to distinguish betweenmixed co-infections and pure viral infections, as only the former shouldbe treated with antibiotics. Correct diagnosis of mixed co-infection ischallenging, because the dual response of the host to the bacterial andviral infections may alter the immune-signature.

We tested the ability of the TCM-signature to distinguish between mixedco-infections and pure viral infections using a leave-10%-outcross-validation scheme in a cohort of patients whose diagnosis wasdetermined as viral or mixed by the majority of experts in our panel(the ‘Majority [viral, mixed]’ cohort). The diagnostic accuracy in termsof AUC was 0.97, 0.93, and 0.95 in children, adults, and all ages,respectively, demonstrating the ability of the TCM-signature tosuccessfully distinguish between these two infection types (FIG. 28,Table 8).

Diagnostic Accuracy Remains Robust when Testing Cohorts that IncludePatients that were Initially Excluded from the Study

The TCM-signature was originally designed to diagnose patients withacute bacterial/viral infections that adhere to a pre-defined list ofinclusion/exclusion criteria.

We tested the ability of the TCM-signature to diagnose the excludedpatients (e.g., patients with fever below 37.5° C.) by adding theexcluded patients (for whom a diagnosis was determined unanimously or bymajority of our expert panel) to the ‘Consensus (bacterial, viral)’cohort and the ‘Majority (bacterial, viral)’ cohort, respectively andcomparing the diagnostic accuracy before and after the addition, usingthe leave-10%-out cross-validation scheme (Table 9 and FIG. 29). Theaccuracy in the ‘Consensus (bacterial, viral)’ cohort with (n=368) andwithout (n=343) the excluded patients remained the same (AUC of 0.97 inboth cases). The accuracy in the ‘Majority (bacterial, viral)’ cohortwas also similar with (n=450) and without (n=504) the excluded patients(AUC of 0.95 vs 0.94). Thus, the TCM-signature performance remainedrobust even after adding the excluded patients to the analysis.

By Excluding Patients with Marginal DETERMINANT-Signatures the Level ofDiagnostic Accuracy can be Increased

By excluding patients with marginal DETERMINANT-signatures (i.e.DETERMINANT-signatures that yield intermediate scores, such as scoresthat are neither characteristic of viral nor bacterial behavior), onecan further improve the levels of diagnostic accuracy (for example seeTable 14-15 and FIGS. 39-40).

EXAMPLE 9. The Diagnostics Accuracy of DETERMINANT Signatures RemainsRobust Across Different Patient Subgroups

We asked whether the diagnostic accuracy of the DETERMINANT signaturesremains robust across different patient subgroups and clinical settings.To this end, we stratified the patients according to a wide range ofpatient characteristics including time from symptom onset, the specificclinical syndrome, maximal temperature, pathogen subfamily,comorbidities, and treatment with drugs for chronic diseases, and foundthat the diagnostic accuracy remained robust. By way of example and notlimitation, the following section that the TCM-signature diagnosticaccuracy is robust across different patient subgroups. We observedrobust levels of accuracy in other DETERMINANT signatures includingwithout limitation: (TRAIL and CRP), (TRAIL and CRP and SAA), (TRAIL andCRP and Age), (TRAIL and CRP and SAA and Age) (TRAIL, CRP, SAA,Mac-2BP), (TRAIL and CRP and SAA and IL1RA) as well as (TRAIL and CRPand SAA and IP-10). These results further demonstrate the diagnosticsutility of some embodiments of the present invention in the context ofthe real clinical setting and its inherent complexity that stems frompatient heterogeneity.

Stratification Based on Time from Onset of Symptoms

The levels of molecules that participate in the immune response to aninfection usually exhibit a temporal behavior (e.g., different antibodyisotypes such as IgM and IgG show distinct temporal responses toinfection onset). Not surprisingly, we found that many of the analytestested in the present study exhibited various temporal dynamics afterinitial appearance of symptoms. The DETERMINANT signatures aims tomaintain accuracy levels that are invariant to time from symptoms onset(up to 10 days), by considering the levels of multiple analytes withdifferent temporal dynamics, which are used to compensate one another.

To examine the performance of the DETERMINANT signatures as a functionof time from onset of symptoms, we stratified all patients in the‘Majority (bacterial, viral)’ cohort according to the time from theinitial appearance of symptoms (0-2, 2-4, 4-6, and 6-10 days) and testedthe DETERMINANT signatures performance in each subgroup. The accuracyremained roughly the same across the evaluated subgroups (for example,the performance of the TCM-signature is depicted in FIG. 30 and Table10A), indicating that the performance is generally robust in the first10 days after symptom onset.

We examined the accuracy of the DETERMINANT signatures in infectionsoccurring in different physiological systems and clinical syndromes(Table 10B). The TCM-signature demonstrated very high accuracy inrespiratory and systemic infections (AUC of 0.95 and 0.96, respectively)and slightly lower accuracy in gastrointestinal infections (AUC of0.89). The TCM-signature performance was also robust in differentclinical syndromes including fever without source, community acquiredpneumonia, and acute tonsillitis (AUCs of 0.96, 0.94, and 0.94,respectively). Other panels, including panels that measured CRP andTRAIL, showed similar robust results.

Maximal Temperature Stratification

The accuracy of diagnostic assays may depend on disease severity. Theseverity of an infectious disease could be assessed using the maximalcore body temperature measured during the infection. We examined whetherthe DETERMINANT signatures performance depends on patients' fever, bystratifying the patients in the ‘Majority (bacterial, viral)’ cohortbased on their maximal temperature and testing the performance in eachgroup. We found that the diagnostic accuracy in patients with high fever(>39° C.) was similar to that observed in patients with low-to-mediumfever (38-39° C.), (for example AUC of the TCM-signature was 0.956 and0.952, respectively) (FIG. 31).

Since children tend to have higher fevers than adults, we divided thecohort to children (≦18 years) and adults (>18 years) and repeated theanalysis. Again, no significant difference in the DETERMINANT signaturesperformance was observed for patients with high vs low-to-medium fever(FIG. 30).

Pathogen Subfamily Stratification

A total of 44 different pathogens strains were isolated from thepatients enrolled in the current study. We assessed the DETERMINANTsignatures performance on different strains.

To this end, patients from the ‘Majority (bacterial, viral, mixed)’cohort with a positive isolation were stratified according to theisolated pathogen. Each bacterial strain was tested against all viralpatients and each viral strain was tested against all bacterial patients(for example see Table 10C). We observe robust results across a widerange of pathogens with a mean AUC of 0.94.

Accurately Diagnosing Adenoviruses—a Viral Subgroup that is ParticularlyChallenging to Diagnose

Adenoviruses are a subgroup of viruses that are particularly challengingto diagnose because they induce clinical symptoms and lab results thatoften mimic those induced by a bacterial infection. Consequently,adenovirus infections are often treated as a bacterial infection (Kunze,Beier, and Groeger 2010). Furthermore, this subgroup is particularlyimportant because of their wide prevalence in children (5-15% of therespiratory and gastrointestinal infections in children) (Kunze, Beier,and Groeger 2010). We tested DETERMINANT signatures accuracy in children(age ≦18 years) with any bacterial infection vs children with viralinfections and a positive isolation of an adenovirus (79 and 27children, respectively). The DETERMINANT signatures achievedsignificantly higher accuracy levels compared with standard clinical andlaboratory parameters (for example see Table 10D).

Accurately Diagnosing Atypical Bacteria

Atypical bacterial infections often cause clinical symptoms resemblingthose of a viral infection, thus posing a clinical diagnostic challenge(Principi and Esposito 2001). Patients infected with atypical bacteriacould benefit from macrolides antibiotics; yet, they are often leftuntreated (Marc et al. 2000). Additionally, patients with viralinfections are often suspected of having atypical bacteria leading toerroneous administration of antibiotics (Hersh et al. 2011). We testedthe DETERMINANT signatures accuracy in 23 patients that were infectedwith atypical bacterial (16 Mycoplasma pneumonia, 4 Chlamydia pneumonia,2 Legionella pneumophila, and 1 Rickettsia coroni) vs 242 viralpatients. The same test was performed using standard clinical andlaboratory parameters. Results are summarized in Table 10E. For example,the performance of the TCM-signature was significantly better than thatof any of the clinical and lab parameters (P<0.001 when comparing any ofthe clinical or lab parameter AUCs to that the TCM-signature).

Comorbidity-Based Stratification

In real-world clinical practice, patients often have backgroundcomorbidities, which could, potentially, affect the level of analytesmeasured by the DETERMINANT signatures. We therefore examined whetherparticular comorbidities impact the performance of the DETERMINANTsignatures. To this end, we analyzed the most prevalent comorbidities inour patient cohort: hypertension, hyperlipidemia, obesity, asthma,atherosclerosis-related diseases (e.g., ischemic heart disease,myocardial infarction and cerebrovascular accident), diabetes mellitus2, and inflammatory diseases (e.g., rheumatoid arthritis, ulcerativecolitis, Behcet's disease, Crohn's disease, diabetes mellitus 1,fibromyalgia, and familial Mediterranean fever [FMF]). For each of thesecomorbidities, we examined the concentrations of the analytes buildingsome of the DETERMINANT signatures and searched for differences inanalyte levels between patients with and without the comorbidity.Specifically, patients were first divided by disease type (bacterial ormixed, viral, and non-infectious disease). For each of thecomorbidities, patients were further divided according to whether theyhad it (target group) or not (background group). Since somecomorbidities are age dependent, we controlled for age differences inthe target and background groups by computing a characteristic ageinterval in the target group (mean±2×SD) and excluded any patients thatfell outside this interval in both the target and background groups.Next, we tested whether the concentrations of the analytes building someof the DETERMINANT signatures were different in the target vs thebackground groups using WS P-values (Table 10F). None of the evaluatedcomorbidities were associated with significant alterations in the levelsof signature analytes (target vs background groups), indicating that theanalytes building the DETERMINANT signatures are by and largeinsensitive to the evaluated comorbidities.

Stratification by Chronic Drug Regimens

In real-world clinical practice, patients are often under variouschronic drug regimens, which could, potentially, affect the level ofanalytes included in the DETERMINANT signatures. We therefore examinedwhether specific drugs impact the performance of the DETERMINANTsignatures by performing the same analysis as for the comorbidities (seeabove). We examined the following drugs: statins (Simvastatin,Pravastatin, Lipitor, and Crestor), diabetes-related drugs (insulin,Metformin, Glyburide, Repaglinide, Sitagliptin, and Acarbose), betablockers (Atenolol, Carvedilol, Metoprolol, Normalol, Propranolol, andBisprolol), Aspirin, antacids (Omeprazole, Ranitidine, and Famotidine),inhaled corticosteroids (Budesonide, Salmeterol, Budesonide incombination with formoterol, and Hydrocortisone), bronchodilators(Ipratropium, Salbutamol, and Montelukast) and diuretics (Furosemide,Disothiazide, and Spironolactone). Table 10G depicts the WS P-values forcomparing analyte concentrations measured in patients who were under aspecific drug regimen vs those who were not. None of the evaluated druggroups were associated with significant alterations in the levels of theDETERMINANT signatures analytes.

Sepsis Based Stratification

Sepsis is a potentially fatal medical condition characterized by awhole-body inflammatory state (called systemic inflammatory responsesyndrome [SIRS]) and the presence of a known or suspected infection(Levy et al. 2003). Patients with a bacterial sepsis benefit from earlyantibiotic therapy; delayed or misdiagnosis can have serious or evenfatal consequences (Bone et al. 1992; Rivers et al. 2001). We focused onadult patients for whom the definition of SIRS is clear and examined theability of the DETERMINANT signatures to distinguish between adultpatients with bacterial sepsis and those with viral infections as wellas between adult patients with bacterial sepsis and those with viralsepsis.

Adult patients with bacterial sepsis were defined according to theAmerican College of Chest Physicians and the Society of Critical CareMedicine (Bone et al. 1992). SIRS was defined by the presence of atleast two of the following findings: (i) body temperature <36° C.or >38° C., (ii) heart rate >90 beats per minute, (iii) respiratoryrate >20 breaths per minute or, on blood gas, a PaCO2<32 mm Hg (4.3kPa), and (iv) WBC <4,000 cells/mm3 or >12,000 cells/mm3 or >10% bandforms. We found that the DETERMINANT signatures achieved very highlevels of accuracy in distinguishing between adult patients withbacterial sepsis and those with viral infections (for example theTCM-signature showed an AUC of 0.98 and 0.96 for the ‘Consensus [adultbacterial sepsis, adult viral]’ and the ‘Majority [adult bacterialsepsis, adult viral]’ cohorts, respectively, Table 10H). We observedsimilar results for distinguishing between patients with bacterialsepsis and those with viral sepsis (AUC of 0.97 and 0.95 for the‘Consensus [adult bacterial sepsis, adult viral sepsis]’ and the‘Majority [adult bacterial sepsis, adult viral sepsis]’ cohorts,respectively). These results demonstrate the utility of the DETERMINANTsignatures in differentiating adult patients with bacterial sepsis fromadult patients with viral infections.

EXAMPLE 10: The DETERMINANT Signatures Performance Remains Robust AcrossDifferent Clinical Sites and Settings

Clinical-Setting Based Stratification

We compared the DETERMINANT signatures performance in the followingclinical settings: Emergency setting (i.e., pediatric ED [PED] and ED)and non-emergency setting (i.e., pediatrics and internal departments)(Table 11). Performances in the emergency and non-emergency settingswere similar (for example TCM-signature had an AUC of 0.95 vs 0.96 inthe ‘Consensus [bacterial, viral]’ cohort, and 0.92 vs 0.91 in the‘Majority [bacterial, vital, mixed]’ cohort, respectively).

In addition, we compared the DETERMINANT signatures performance inpatients enrolled in two different hospitals and found that theperformance was similar across sites (Table 12).

EXAMPLE 11: Determinant Levels Change as a Function of Age

We examined the DETERMINAT levels of viral and bacterial patients as afunction of age. We found that the levels of many DETERMINANTS are agedependent. For example, the levels of viral induced DETERMINANTS RSAD2,MX1, TRAIL and Mac-2BP show relatively high levels in young children,followed by a gradual decrease with age. In contrast the DETERMINANTlevels of CHI3L1 increases with age. FIG. 32 shows examples ofDETERMINANT levels in different infections as a function of Age. Thisfinding can be used to improve the accuracy of DETERMINANTS fordifferentiating between different types of infections by performing agedependent normalization or stratification (i.e. age dependentnormalization or stratification). For example, one skilled in the artcan generate a function that fits the population mean levels of eachDETERMINANT as function of age and uses it to normalize the DETERMINANTof individual subjects levels across different ages. Another way toimprove diagnostic accuracy is to stratify subjects according to theirage and determine thresholds or index values for each age groupindependently. For example, when testing the DETERMINANT accuracy onlyon young children (age 0-5 years) the following DETERMINANTS improvedtheir accuracy: TRAIL (0.9 to 0.93 AUC), RSAD2 (0.81 to 0.83 AUC) andMac-2BP (0.78 to 0.85 AUC).

EXAMPLE 12: Performance is Robust to the Presence of Bacteria andViruses that are Part of the Natural Flora

Many disease-causing pathogens are also part of the natural flora, andare frequently found in healthy individuals and in patients withnon-infectious diseases (Vaneechoutte et al. 1990; Regev-Yochay et al.2004; Shaikh, Leonard, and Martin 2010). These non-pathogenic bacteriaand viruses, termed colonizers, pose a considerable diagnostic challengebecause their presence does not necessarily imply pathogenicity. Inother words, merely isolating these bacterial/viral strains from apatient does not necessarily indicate that they are the disease-causingagents; therefore, the appropriate treatment may remain unclear.

We investigated whether the DETERMINANT signatures performance isinfluenced by colonization, focusing on the most prevalent bacterialstrains in our patient cohort, Streptococcus pneumoniae (SP) andHaemophilus influenzae (HI), and the viral strain Rhinovirus A/B/C. Todetect these strains, we applied multiplex-PCR to the nasopharyngealwash of the ‘Majority (bacterial, viral, mixed, non-infectious)’ cohort.First, we examined the prevalence of these strains in patients withnon-infectious diseases (n=46) (FIG. 33A). Isolation rate was higher(about 5-fold) in children (<18 years) then in adults (>18 years), inaccordance with previous studies (Regev-Yochay et al. 2012). Next, weexamined the prevalence of these strains in patients with bacterial(n=208), viral (n=242), and mixed (n=34) infections as determined by themajority of our expert panel (FIG. 33B and Table 13). The bacterialstrains SP and HI were highly prevalent in viral patients (51% and 36%,respectively) and rhinovirus A/B/C was detected in 4% of the bacterialpatients. Thus, bacterial or viral etiologies cannot be inferred merelybased on isolation of a specific strain.

To test whether the DETERMINANT signatures performance is influenced bySP colonization, we stratified the patients based on SP colonization andexamined the accuracy of the DETERMINANT signatures (viral vs bacterial)in each group separately. For example, we found that the TCM-signatureperformed similarly in both groups (AUC of 0.95±0.03 vs 0.94±0.04 in thegroups with and without SP colonization, respectively). We used the sameapproach to evaluate the impact of HI and rhinovirus A/B/C colonizationand the findings were comparable (FIG. 34). Thus, our findings indicatethat the DETERMINANT signatures performance is robust to thecolonization of patients by SP, HI, or rhinovirus A/B/C.

EXAMPLE 13: Trail is an Effective Polypeptide for Diagnosing ViralInfections

In a setting where resources are limited (e.g., a family physician'soffice), it may be advantageous to have a rapid, easy-to-perform assay,even at the cost of a reduced diagnostic accuracy. In this section, weexplore the accuracy of TRAIL as a single polypeptide, to detect viralinfections. Although the accuracy of TRAIL is lower than that of someDETERMINANT signatures, it requires the measurement of a singlepolypeptide and is thus readily measurable on a wide range of machinesincluding lateral flow immunoassay analyzers that are widely spread atthe point-of-care setting.

We examined the diagnostic utility of TRAIL using the ‘Consensus(bacterial, viral)’ cohort (n=343, 153 bacterial and 190 viral) andfound that TRAIL concentrations were substantially higher in viral vsbacterial patients (t-test P<10⁻²³) (FIG. 35) and that the AUC was 0.9(FIG. 36).

One application of the TRAIL-based assay is to rule out bacterialinfections (e.g., using a cutoff that produces a sensitivity of 97% andspecificity of 55%; FIG. 36). In an outpatient setting where the ratiobetween bacterial and viral infections is ˜1:4, this would translate toan NPV of 99% and PPV of 35%. Thus, antibiotics can be withheld in caseof a negative test result, whereas a positive test result would requirean additional workup to facilitate an informed treatment decision.

Excluding patients with marginal TRAIL calls (i.e., patients that fallnear the cutoff), can further increase the level of accuracy. Thebalance between the number of patients diagnosed and the accuracy of theassay is depicted in FIG. 37.

Interestingly, when comparing TRAIL levels across different patientsubgroups we found that its concentrations were highest in viralpatients (median of 121±132 pg/ml), lower in healthy and non-infectiouspatients (median of 88±41 pg/ml), and lowest in bacterial patients(52±65 pg/ml). These results suggest that not only does viral infectionsup-regulate TRAIL levels, but also that bacterial infectionsdown-regulate them. The finding that bacterial infections down regulateTRAIL is further supported by our observation that in viral andbacterial co-infections (i.e. mixed infections) TRAIL levels are low(which may be due to bacterial response dominance). Altogether, inaddition to TRAIL's up-regulation in viral infections, its downregulation in bacterial infections, contribute to its ability toaccurately distinguish between viral and bacterial infections. Thispoint is further illustrated in FIG. 41.

Of note, TRAIL dynamics is correlated with the disease stage (FIG. 41).Thus TRAIL can be used not only for diagnosis of infection, but also foridentifying disease stage and prognosis.

Tables

In the following tables the abbreviations mono, lymp, gran, mean andtotal are used to denote polypeptide-DETERMINANT measurements onmonocytes, lymphocytes, granulocytes as well as mean and totalleukocytes measurements respectively. The abbreviations intra andmembrane are used to denote proteins that were measured in the intracellular and membrane fraction respectively.Table 1. Examples of polypeptide-DETERMINANTS with an immunological rolethat do not differentiate between bacterial versus viral infectedsubjects.

Positives and negatives correspond to bacterial and viral infectedpatients respectively. Positives (P) and Negatives (N) correspond tobacterial and viral infected patients respectively. TA, Sen, Spe and log2(R) correspond total accuracy sensitivity, specificity and log 2 ratiobetween medians of the positive and negative classes respectively.

t-test DETERMINANT AUC P-value MCC TA % Sen % Spe % PPV % NPV % P Nlog2(R) sIL-2Ra, soluble 0.53 2.2E−01 0.19 57 76 42 52 69 21 26 −0.27IL-9, soluble 0.52 1.1E−01 0.13 55 67 46 50 63 21 26 −0.31 IL-8, soluble0.66 9.2E−01 −0.23 38 43 35 35 43 21 26 −0.48 IL-4, soluble 0.64 1.9E−010.07 49 86 19 46 63 21 26 0.00 IL-33, soluble 0.55 7.6E−01 0.08 55 38 6950 58 21 26 0.61 IL-3, soluble 0.54 1.9E−01 0.01 49 67 35 45 56 21 26−0.11 IL-28A, soluble 0.50 5.3E−01 0.03 51 57 46 46 57 21 26 0.00 IL-23,soluble 0.58 5.9E−01 −0.05 50 24 72 42 53 21 25 0.41 IL-21, soluble 0.554.2E−01 0.11 57 33 77 54 59 21 26 −0.06 IL-20, soluble 0.57 3.2E−01 0.0351 57 46 46 57 21 26 −0.16 IL-2, soluble 0.51 1.3E−01 −0.04 47 62 35 4353 21 26 0.22 IL-1ra, soluble 0.56 5.8E−01 −0.08 45 62 31 42 50 21 26−0.27 IL, soluble 0.76 4.2E−01 −0.35 32 43 23 31 33 21 26 0.08 IL17A,soluble 0.76 8.3E−01 −0.31 34 57 15 35 31 21 26 0.42 IL-16, soluble 0.657.1E−01 −0.16 40 62 23 39 43 21 26 −0.06 IL-15, soluble 0.56 2.8E−01 049 62 38 45 56 21 26 0.00 IL-13, soluble 0.47 2.5E−01 0.12 53 76 35 4864 21 26 −0.33 IL12(p70), soluble 0.76 9.2E−01 −0.43 30 14 44 18 38 2125 0.28 CDH23, mono, membrane 0.53 4.5E−01 0.04 51 47 57 64 39 38 230.20 CDH23, mean, membrane 0.56 2.8E−01 0.14 55 49 65 69 44 37 23 0.14CDH23, lymp, membrane 0.54 1.7E−01 0.09 53 49 61 67 42 37 23 0.08 CDH23,gran, membrane 0.56 2.5E−01 0.18 61 66 52 69 48 38 23 −0.15 CD99R, mono,membrane 0.47 4.6E−01 0.11 59 76 34 64 48 45 29 −0.34 CD99R, mean,membrane 0.53 4.9E−01 0.12 58 64 48 65 47 44 29 −0.14 CD99R, gran,membrane 0.57 6.2E−01 −0.03 50 56 41 60 38 45 29 −0.08 CD69, gran,membrane 0.55 6.0E−01 0.13 56 63 50 56 57 8 8 0.23 CD66F, gran, membrane0.77 8.8E−01 −0.25 38 38 38 38 38 8 8 0.01 CD64, lymp, membrane 0.696.4E−01 −0.13 44 63 25 45 40 8 8 −0.07 CD62P, lymp, membrane 0.758.9E−01 −0.52 25 38 13 30 17 8 8 −1.74 CD62P, gran, membrane 0.777.4E−01 −0.26 38 25 50 33 40 8 8 −1.60 CD62L, lymp, membrane 0.758.0E−01 −0.26 38 50 25 40 33 8 8 0.21 CD62L, gran, membrane 0.88 8.8E−01−0.63 19 25 13 22 14 8 8 −0.04 CD62E, lymp, membrane 0.69 3.6E−01 −0.2638 50 25 40 33 8 8 −0.06 CD62E, gran, membrane 0.92 7.4E−01 −0.63 19 2513 22 14 8 8 0.50 CD61, gran, membrane 0.50 1.5E−01 0.13 56 38 75 60 558 8 −0.06 CD57, lymp, membrane 0.70 7.2E−01 −0.25 38 38 38 38 38 8 80.22 CD57, gran, membrane 0.91 8.7E−01 −0.52 25 13 38 17 30 8 8 −0.25CD56, gran, membrane 0.63 5.7E−01 0 50 63 38 50 50 8 8 −0.06 CD55, lymp,membrane 0.58 7.7E−01 0.13 56 63 50 56 57 8 8 0.12 CD55, gran, membrane0.77 7.3E−01 −0.29 38 13 63 25 42 8 8 0.00 CD54, mono, membrane 0.587.0E−01 −0.07 47 51 41 57 35 45 29 −0.15 CD54, mean, membrane 0.638.4E−01 −0.16 42 45 38 53 31 44 29 0.18 CD54, lymp, membrane 0.555.3E−01 −0.11 45 45 43 54 35 44 30 0.21 CD54, gran, membrane 0.669.7E−01 −0.14 44 49 37 54 32 45 30 0.17 CD53, lymp, membrane 0.818.9E−01 −0.25 38 38 38 38 38 8 8 −0.12 CD51/CD61, gran, 0.48 3.1E−010.13 56 63 50 56 57 8 8 −0.30 membrane CD50, lymp, membrane 0.80 7.8E−01−0.26 38 50 25 40 33 8 8 −0.01 CD50, gran, membrane 0.53 4.7E−01 0.13 5663 50 56 57 8 8 0.07 CD5, lymp, membrane 0.77 9.1E−01 −0.26 38 25 50 3340 8 8 −0.09 CD49E, gran, membrane 0.83 4.9E−01 −0.25 38 38 38 38 38 8 8−0.30 CD49D, lymp, membrane 0.75 5.5E−01 −0.29 38 13 63 25 42 8 8 −0.06CD49D, gran, membrane 0.84 7.1E−01 −0.52 25 13 38 17 30 8 8 0.17 CD49C,lymp, membrane 0.77 5.9E−01 −0.25 38 38 38 38 38 8 8 −0.13 CD49C, gran,membrane 0.84 9.9E−01 −0.38 31 25 38 29 33 8 8 −0.22 CD49A, gran,membrane 0.73 3.5E−01 0.16 56 88 25 54 67 8 8 0.25 CD491, mono, membrane0.48 4.7E−01 0.13 56 63 50 56 57 8 8 −0.41 CD491, mean, membrane 0.776.7E−01 −0.26 38 25 50 33 40 8 8 0.08 CD491, gran, membrane 0.77 7.1E−01−0.38 31 25 38 29 33 8 8 −0.21 CD48, gran, membrane 0.84 9.8E−01 −0.6319 13 25 14 22 8 8 −0.08 CD47, lymp, membrane 0.70 7.8E−01 −0.13 44 5038 44 43 8 8 0.12 CD47, gran, membrane 0.55 6.8E−01 −0.29 38 63 13 42 258 8 0.09 CD46, gran, membrane 0.52 2.1E−01 0.16 56 88 25 54 67 8 8 −0.05CD45RO, lymp, 0.92 8.0E−01 −0.63 19 13 25 14 22 8 8 −0.19 membraneCD45RB, lymp, 0.58 6.3E−01 0.13 56 75 38 55 60 8 8 0.07 membrane CD45RA,lymp, 0.66 7.7E−01 −0.13 44 50 38 44 43 8 8 −0.02 membrane CD45RA, gran,membrane 1.00 1.0E+00 −0.88 6 13 0 11 0 8 8 0.22 CD45, mono, membrane0.52 7.1E−01 −0.07 46 52 41 41 52 54 68 −0.09 CD45, mean, membrane 0.524.5E−01 0.03 49 70 32 45 58 54 68 0.14 CD45, gran, membrane 0.57 6.6E−01−0.05 46 63 32 42 52 54 69 0.34 CD44, lymp, membrane 0.83 5.6E−01 −0.6319 13 25 14 22 8 8 −0.07 CD43, lymp, membrane 0.73 5.9E−01 −0.13 44 3850 43 44 8 8 −0.35 CD41b, gran, membrane 0.70 4.5E−01 −0.13 44 25 63 4045 8 8 −0.08 CD41a, lymp, membrane 0.56 2.8E−01 0.16 56 88 25 54 67 8 8−0.31 CD40, gran, membrane 0.80 9.7E−01 −0.38 31 25 38 29 33 8 8 −0.09CD4, lymp, membrane 0.61 8.9E−01 0 50 38 63 50 50 8 8 −0.25 CD4, gran,membrane 0.88 8.4E−01 −0.52 25 13 38 17 30 8 8 0.06 CD39, gran, membrane0.77 8.7E−01 −0.38 31 38 25 33 29 8 8 0.17 CD38, mono, membrane 0.739.8E−01 −0.36 32 36 28 43 22 45 29 −0.25 CD38, gran, membrane 0.523.6E−01 0.01 52 58 43 60 41 45 30 0.09 CD37, lymp, membrane 0.45 4.6E−010.15 56 68 46 49 66 41 54 −0.18 CD36, lymp, membrane 0.58 4.7E−01 0.1656 25 88 67 54 8 8 −0.30 CD337-PE, lymp, membrane 0.52 4.4E−01 −0.06 4746 48 59 35 37 23 −0.31 CD33, gran, membrane 0.63 3.7E−01 0.16 56 25 8867 54 8 8 −0.08 CD326, mean, membrane 0.55 3.4E−01 0 50 25 75 50 50 8 80.05 CD326, gran, membrane 0.52 3.2E−01 0.16 56 25 88 67 54 8 8 −0.07CD32, lymp, membrane 0.81 9.6E−01 −0.63 19 13 25 14 22 8 8 −0.11 CD31,lymp, membrane 0.64 2.2E−01 −0.16 44 13 75 33 46 8 8 −0.05 CD30, lymp,membrane 0.83 8.0E−01 −0.52 25 38 13 30 17 8 8 0.13 CD3, lymp, membrane0.61 4.7E−01 −0.13 44 38 50 43 44 8 8 −0.05 CD294, mean, membrane 0.727.9E−01 −0.29 38 13 63 25 42 8 8 0.13 CD294, gran, membrane 0.81 9.1E−01−0.38 31 25 38 29 33 8 8 −0.12 CD28, gran, membrane 0.58 5.0E−01 0.16 5688 25 54 67 8 8 0.03 CD275, mean, membrane 0.64 7.3E−01 −0.13 44 38 5043 44 8 8 −0.06 CD275, gran, membrane 0.56 5.9E−01 −0.13 44 38 50 43 448 8 0.09 CD274, gran, membrane 0.64 3.9E−01 −0.38 38 75 0 43 0 8 8 −0.18CD27, gran, membrane 0.86 7.0E−01 −0.52 25 38 13 30 17 8 8 −0.14 CD267,mean, membrane 0.92 7.7E−01 −0.29 38 13 63 25 42 8 8 −0.09 CD267, gran,membrane 0.75 7.5E−01 −0.26 38 25 50 33 40 8 8 −0.12 CD26, gran,membrane 0.59 6.7E−01 0 50 63 38 50 50 8 8 −0.05 CD25, lymp, membrane0.95 9.1E−01 −0.77 13 25 0 20 0 8 8 −0.15 CD25, gran, membrane 0.757.5E−01 −0.26 38 50 25 40 33 8 8 0.07 CD244, mono, membrane 0.72 7.0E−01−0.26 38 25 50 33 40 8 8 0.15 CD244, mean, membrane 0.86 7.7E−01 −0.2638 50 25 40 33 8 8 0.34 CD244, gran, membrane 0.88 7.9E−01 −0.48 31 63 038 0 8 8 0.49 CD243, mono, membrane 0.78 8.8E−01 −0.38 31 38 25 33 29 88 −0.11 CD243, mean, membrane 0.86 7.1E−01 −0.63 19 25 13 22 14 8 8−0.09 CD243, gran, membrane 0.88 7.8E−01 −0.63 19 25 13 22 14 8 8 0.00CD235A, mono, 0.86 8.2E−01 −0.58 25 0 50 0 33 8 8 0.02 membrane CD235A,lymp, 0.88 8.0E−01 −0.5 25 25 25 25 25 8 8 −0.48 membrane CD226, gran,membrane 0.70 3.8E−01 −0.26 44 88 0 47 0 8 8 −0.12 CD22, gran, membrane0.53 2.9E−01 0.13 56 38 75 60 55 8 8 0.27 CD212, mono, membrane 0.817.6E−01 −0.52 25 38 13 30 17 8 8 −0.31 CD212, mean, membrane 0.737.1E−01 −0.26 38 25 50 33 40 8 8 0.00 CD212, gran, membrane 0.81 8.0E−01−0.38 31 25 38 29 33 8 8 −0.14 CD210, mono, membrane 0.80 8.2E−01 −0.2538 38 38 38 38 8 8 0.04 CD210, mean, membrane 0.80 7.6E−01 −0.13 44 2563 40 45 8 8 0.11 CD210, gran, membrane 0.86 9.6E−01 −0.52 25 13 38 1730 8 8 −0.15 CD21, gran, membrane 0.47 6.1E−01 0.13 56 38 75 60 55 8 8−0.06 CD205, mono, membrane 0.52 6.8E−01 0.02 53 62 40 61 41 45 30 −0.30CD205, mean, membrane 0.57 2.5E−01 0.1 57 64 47 64 47 44 30 −0.44 CD205,lymp, membrane 0.59 8.5E−01 −0.09 49 61 30 56 35 44 30 −0.06 CD201,mono, membrane 0.89 6.2E−01 −0.4 31 13 50 20 36 8 8 0.47 CD201, mean,membrane 0.80 6.4E−01 −0.48 31 0 63 0 38 8 8 0.12 CD201, lymp, membrane0.80 8.7E−01 −0.67 19 38 0 27 0 8 8 −0.14 CD201, gran, membrane 0.807.7E−01 −0.38 31 25 38 29 33 8 8 −0.17 CD200, lymp, membrane 0.645.4E−01 −0.13 44 63 25 45 40 8 8 0.07 CD20, lymp, membrane 0.83 7.0E−01−0.38 31 25 38 29 33 8 8 −0.34 CD20, gran, membrane 0.64 5.1E−01 0.13 5675 38 55 60 8 8 −0.03 CD2, gran, membrane 0.64 6.4E−01 −0.26 38 25 50 3340 8 8 −0.10 CD1D, gran, membrane 0.59 6.2E−01 −0.13 44 38 50 43 44 8 8−0.28 CD1B, gran, membrane 0.69 9.8E−01 −0.38 31 38 25 33 29 8 8 −0.15CD195, mean, membrane 0.73 6.0E−01 −0.26 38 50 25 40 33 8 8 −0.06 CD195,gran, membrane 0.95 9.2E−01 −0.77 13 25 0 20 0 8 8 0.13 CD19, gran,membrane 0.66 8.3E−01 −0.25 38 38 38 38 38 8 8 0.00 CD184, mono,membrane 0.80 9.9E−01 −0.52 25 13 38 17 30 8 8 −0.43 CD184, mean,membrane 0.66 7.5E−01 0 50 25 75 50 50 8 8 −0.14 CD184, lymp, membrane0.73 8.7E−01 −0.13 44 38 50 43 44 8 8 −0.49 CD184, gran, membrane 0.556.2E−01 0 50 38 63 50 50 8 8 0.08 CD183, mono, membrane 0.92 8.7E−01−0.52 25 38 13 30 17 8 8 0.21 CD183, mean, membrane 0.73 4.7E−01 −0.2938 63 13 42 25 8 8 0.09 CD182, mean, membrane 0.57 5.0E−01 −0.22 43 5623 54 24 36 22 0.02 CD182, gran, membrane 0.54 2.3E−01 −0.06 51 62 32 6133 37 22 0.01 CD181, mono, membrane 0.58 8.4E−01 −0.03 48 47 50 58 38 4530 −0.16 CD181, mean, membrane 0.53 5.4E−01 0.05 51 45 60 63 43 44 300.08 CD181, gran, membrane 0.59 7.7E−01 −0.1 45 47 43 55 35 45 30 −0.06CD180, mono, membrane 0.57 2.9E−01 0.09 55 56 53 64 44 45 30 −0.58CD180, mean, membrane 0.55 2.8E−01 0.09 57 66 43 63 46 44 30 −0.21CD180, lymp, membrane 0.55 4.8E−01 −0.06 46 41 53 56 38 44 30 0.16CD180, gran, membrane 0.56 1.9E−01 0.11 59 73 37 63 48 45 30 −0.27CD177, mono, membrane 0.67 7.7E−01 −0.38 31 25 38 29 33 8 8 −0.78CD172B, mono, 0.98 6.5E−01 −0.88 6 0 13 0 11 8 8 0.10 membrane CD171,mono, membrane 0.50 6.3E−01 0.13 56 50 63 57 56 8 8 −0.73 CD171, mean,membrane 0.86 7.3E−01 −0.4 31 50 13 36 20 8 8 0.24 CD171, gran, membrane0.88 7.1E−01 −0.48 31 63 0 38 0 8 8 0.36 CD166, mono, membrane 0.787.5E−01 −0.13 44 25 63 40 45 8 8 −0.17 CD166, mean, membrane 0.818.3E−01 −0.5 25 25 25 25 25 8 8 0.32 CD166, gran, membrane 0.88 9.3E−01−0.52 25 38 13 30 17 8 8 0.37 CD165, mono, membrane 0.52 4.0E−01 −0.1344 50 38 44 43 8 8 −0.06 CD165, mean, membrane 0.47 3.7E−01 0.13 56 7538 55 60 8 8 0.03 CD165, gran, membrane 0.72 4.2E−01 0 50 88 13 50 50 88 0.16 CD164, mean, membrane 0.80 5.9E−01 −0.26 38 50 25 40 33 8 8 0.02CD164, gran, membrane 0.75 6.8E−01 −0.29 38 63 13 42 25 8 8 −0.14 CD163,mono, membrane 0.72 8.2E−01 −0.25 38 38 38 38 38 8 8 −0.27 CD163, mean,membrane 0.55 3.4E−01 0 50 63 38 50 50 8 8 −0.43 CD162, lymp, membrane0.53 2.9E−01 −0.13 44 50 38 44 43 8 8 −0.06 CD162, gran, membrane 0.564.5E−01 0 50 50 50 50 50 8 8 0.13 CD161, mean, membrane 0.64 3.9E−01−0.16 44 75 13 46 33 8 8 −0.16 CD161, gran, membrane 0.69 3.6E−01 0 5088 13 50 50 8 8 0.05 CD16, gran, membrane 0.86 6.7E−01 −0.5 25 25 25 2525 8 8 −0.16 CD15s, gran, membrane 0.84 5.5E−01 −0.63 19 25 13 22 14 8 8−0.02 CD158B, gran, membrane 0.52 3.7E−01 0.13 56 75 38 55 60 8 8 −0.05CD153, gran, membrane 0.59 3.6E−01 0 50 88 13 50 50 8 8 −0.11 CD152,mono, membrane 0.89 9.3E−01 −0.63 19 13 25 14 22 8 8 −0.13 CD152, mean,membrane 0.95 8.3E−01 −0.67 19 38 0 27 0 8 8 0.38 CD152, gran, membrane0.95 9.6E−01 −0.63 19 25 13 22 14 8 8 0.05 CD151, gran, membrane 0.593.5E−01 0 50 88 13 50 50 8 8 −0.13 CD15, mono, membrane 0.55 6.3E−01−0.08 45 59 33 42 50 56 69 0.09 CD15, lymp, membrane 0.44 4.5E−01 0.0148 68 33 45 56 56 70 −0.27 CD15, gran, membrane 0.64 9.1E−01 −0.2 40 3843 34 46 56 70 0.18 CD147, gran, membrane 0.95 9.5E−01 −0.67 19 38 0 270 8 8 0.57 CD146, gran, membrane 0.52 4.0E−01 −0.13 44 63 25 45 40 8 8−0.24 CD144, gran, membrane 0.84 7.6E−01 −0.52 25 13 38 17 30 8 8 −0.48CD141, mean, membrane 0.92 7.8E−01 −0.4 31 50 13 36 20 8 8 0.54 CD141,gran, membrane 0.94 6.2E−01 −0.26 44 88 0 47 0 8 8 0.62 CD140B, mono,0.64 5.8E−01 −0.13 44 50 38 44 43 8 8 −0.43 membrane CD140B, lymp, 0.523.1E−01 0 50 63 38 50 50 8 8 −0.37 membrane CD140A, mono, 0.70 7.4E−01 050 38 63 50 50 8 8 −0.29 membrane CD140A, mean, 0.91 9.2E−01 −0.77 13 250 20 0 8 8 0.05 membrane CD140A, gran, membrane 0.95 9.6E−01 −0.63 19 2513 22 14 8 8 0.06 CD14, gran, membrane 0.84 6.7E−01 −0.4 31 13 50 20 368 8 0.45 CD137L, mono, 0.72 9.3E−01 −0.38 31 25 38 29 33 8 8 0.02membrane CD137L, mean, 0.61 6.0E−01 −0.26 38 50 25 40 33 8 8 0.11membrane CD137L, gran, membrane 0.75 5.6E−01 −0.16 44 75 13 46 33 8 80.06 CD137, mono, membrane 0.53 7.5E−01 −0.13 44 63 25 45 40 8 8 −0.63CD137, gran, membrane 0.47 4.3E−01 0 50 50 50 50 50 8 8 −0.19 CD135,mono, membrane 0.84 9.1E−01 −0.5 25 25 25 25 25 8 8 0.10 CD127, mono,membrane 0.44 2.2E−01 0.13 56 75 38 55 60 8 8 −0.28 CD127, gran,membrane 0.50 4.6E−01 0 50 50 50 50 50 8 8 −0.12 CD126, mean, membrane0.56 4.7E−01 0.13 56 63 50 56 57 8 8 −0.17 CD126, gran, membrane 0.645.9E−01 −0.26 38 50 25 40 33 8 8 0.00 CD124, mono, membrane 0.53 2.2E−010.13 56 75 38 55 60 8 8 −0.07 CD124, mean, membrane 0.56 3.6E−01 0 50 8813 50 50 8 8 −0.27 CD124, gran, membrane 0.66 3.8E−01 −0.26 44 88 0 47 08 8 0.00 CD123, gran, membrane 0.47 2.7E−01 0 50 75 25 50 50 8 8 −0.02CD120B, mono, 0.69 9.4E−01 −0.25 38 38 38 38 38 8 8 0.27 membraneCD120B, mean, 0.70 5.5E−01 −0.13 44 25 63 40 45 8 8 0.20 membraneCD120B, gran, membrane 0.75 6.1E−01 −0.38 31 25 38 29 33 8 8 −0.11CD11C, mean, membrane 0.55 4.2E−01 −0.03 52 66 31 59 38 44 29 0.14CD11C, lymp, membrane 0.54 5.7E−01 −0.12 51 77 13 57 29 44 30 −0.21CD11C, gran, membrane 0.52 3.2E−01 −0.05 51 62 33 58 37 45 30 −0.12CD11a, lymp, membrane 0.57 2.2E−01 0.13 57 47 66 51 62 47 61 0.11 CD11a,gran, membrane 0.47 4.6E−01 0.12 56 51 61 50 62 47 61 0.11 CD119, mono,membrane 0.55 2.9E−01 0.13 56 50 63 57 56 8 8 −0.05 CD119, mean,membrane 0.72 5.5E−01 0.16 56 88 25 54 67 8 8 0.17 CD119, lymp, membrane0.44 2.6E−01 0.13 56 75 38 55 60 8 8 −0.26 CD119, gran, membrane 0.835.3E−01 −0.38 38 75 0 43 0 8 8 0.25 CD116, mono, membrane 0.47 4.2E−010.13 56 63 50 56 57 8 8 −0.64 CD114, mean, membrane 0.89 8.9E−01 −0.6319 13 25 14 22 8 8 −0.18 CD107A, mono, 0.55 6.5E−01 0.08 54 53 55 65 4345 29 −0.19 membrane CD107A, mean, 0.53 2.9E−01 −0.01 51 55 45 60 39 4429 −0.06 membrane CD107A, gran, membrane 0.43 2.4E−01 −0.12 49 64 24 5730 45 29 0.26 CD104, gran, membrane 0.69 5.8E−01 −0.25 38 38 38 38 38 88 −0.06 CD10, lymp, membrane 0.58 4.6E−01 0 50 25 75 50 50 8 8 0.54

TABLE 2A DETERMINANTS that differentiate between bacterial versus viralinfected subjects. Positives (P) and Negatives (N) correspond tobacterial and viral infected patients respectively. TA, Sen, Spe andlog2(R) correspond total accuracy sensitivity, specificity and log2ratio between medians of the positive and negative classes respectively.t-test TA Spe PPV DETERMINANT AUC P-value MCC % Sen % % % NPV % P Nlog2(R) B2M, soluble 0.45 9.41E−02 0.126 45 62 60 70 51 68 45 0.45BCA-1, soluble 0.65  3.1E−03 0.21 60 72 49 55 66 116 131 −0.50 CHI3L1,soluble 0.77  7.6E−11 0.43 70 44 93 85 65 114 129 1.19 Eotaxin, soluble0.67  4.4E−06 0.28 63 74 54 59 70 118 131 −0.41 IL1a, soluble 0.62 2.2E−02 0.27 58 95 24 53 84 118 131 −0.06 IP10, soluble 0.63  1.8E−020.21 58 85 34 53 71 118 131 −0.85 MCP, soluble 0.74  5.3E−09 0.35 66 8153 61 75 118 130 −0.92 Mac-2BP*, soluble 0.77  1.6E−17 0.43 71 77 66 6677 176 208 Mac-2BP, soluble 0.73  7.0E−19 0.35 68 71 65 65 71 243 268TRAIL, soluble 0.86  0.0E+00 0.56 78 85 71 72 84 118 131 −1.76 (measuredwith Luminex) TRAIL*, soluble 0.89  2.6E−22 0.6 81 84 79 77 85 177 213−1.21 (measured with ELISA) TRAIL, soluble 0.85  3.8E−25 0.52 77 78 7674 80 245 273 −1.18 (measured with ELISA) sCD62L, soluble 0.81  8.0E−060.44 72 72 71 72 71 29 28 −0.29 sVEGFR2, soluble 0.77  7.1E−14 0.46 7282 63 67 80 118 131 −0.45 CHP, total, intra 0.73  2.0E−03 0.23 63 45 7658 66 33 46 1.07 CMPK2, lymp, intra 0.71  2.2E−03 0.34 65 80 54 55 80 5072 −0.55 CORO1C, total, intra 0.71  5.0E−04 0.26 65 52 74 59 68 33 460.82 EIF2AK2, lymp, intra 0.79  2.6E−05 0.47 75 82 65 79 68 38 23 −1.12ISG15, gran, intra 0.76  2.5E−05 0.47 75 84 61 78 70 38 23 −1.22 ISG15,lymp, intra 0.73  1.3E−04 0.47 75 82 65 79 68 38 23 −0.96 ISG15, mean,intra 0.75  7.1E−05 0.42 73 84 57 76 68 37 23 −0.86 ISG15, mono, intra0.75  3.7E−05 0.46 75 84 61 78 70 37 23 −1.16 RPL22L1, lymp, intra 0.69 3.2E−02 0.36 69 48 84 70 69 33 45 1.91 RPL22L1, total, intra 0.74 9.1E−04 0.33 68 55 78 64 70 33 45 1.42 RTN3, lymp, intra 0.75  3.2E−050.53 77 70 83 74 79 33 46 1.21 RTN3, total, intra 0.74  9.3E−05 0.32 6761 72 61 72 33 46 1.03 EIF4B, gra n, intra 0.70  6.8E−04 0.24 60 78 4556 69 86 96 −0.84 EIF4B, lymp, intra 0.73  5.0E−03 0.18 57 81 34 53 6786 96 −0.71 EIF4B, mean, intra 0.68  6.4E−03 0.14 55 75 38 52 63 84 93−0.73 EIF4B, mono, intra 0.70  6.8E−04 0.24 60 78 45 56 69 86 96 −0.84IFIT1, gran, intra 0.74  1.6E−06 0.4 75 84 54 80 62 51 24 −0.59 IFIT1,lymp, intra 0.76  4.2E−07 0.47 78 90 52 79 72 51 25 −0.85 IFIT1, mean,intra 0.77  3.9E−07 0.44 76 84 58 81 64 51 24 −0.91 IFIT1, mono, intra0.74  1.6E−06 0.4 75 84 54 80 62 51 24 −0.59 IFIT3, gran, intra 0.76 2.1E−04 0.32 69 79 52 73 60 38 23 −0.77 IFIT3, lymp, intra 0.73 1.4E−03 0.43 74 84 57 76 68 38 23 −1.09 IFIT3, mono, intra 0.75 2.9E−04 0.32 68 78 52 73 60 37 23 −0.63 LOC26010, gran, intra 0.64 3.9E−04 0.2 59 72 47 55 65 86 96 −0.34 LOC26010, mono, intra 0.64 3.9E−04 0.2 59 72 47 55 65 86 96 −0.34 MBOAT2, total, intra 0.67 1.5E−04 0.22 63 49 72 56 67 59 83 0.49 MX1, gran, intra 0.74  6.7E−100.36 68 78 57 66 72 124 119 −0.90 MX1, lymp, intra 0.71  1.9E−08 0.29 6574 55 63 67 124 119 −0.66 MX1, mean, intra 0.72  9.7E−09 0.37 68 77 5966 71 121 116 −0.92 MX1, mono, intra 0.73  7.9E−10 0.36 68 78 57 65 72123 119 −0.91 OAS2, gran, intra 0.66  6.4E−04 0.22 61 73 48 59 63 124120 −0.63 OAS2, mean, intra 0.61  2.8E−02 0.15 58 70 44 57 59 121 117−0.46 OAS2, mono, intra 0.66  7.1E−04 0.21 60 73 48 59 63 123 120 −0.61PCT, soluble 0.65 0.008626 0.22 59 49 68 56 62 47 57 −0.067 RSAD2, gran,intra 0.81  2.2E−14 0.41 70 79 61 68 74 119 115 −1.50 RSAD2, lymp, intra0.65  6.1E−06 0.19 59 68 50 59 60 119 115 −0.38 RSAD2, mean, intra 0.77 1.6E−11 0.34 67 76 58 65 70 116 112 −1.15 RSAD2, mono, intra 0.81 3.1E−14 0.4 70 79 61 67 74 118 115 −1.50 RSAD2, total, intra 0.66 4.0E−06 0.3 64 78 51 62 69 116 112 −0.67 CD112, lymp, membrane 0.89 6.5E−03 0.6 80 88 71 78 83 8 7 −1.10 CD134, lymp, membrane 0.89 5.4E−03 0.4 69 88 50 64 80 8 8 −1.21 CD182, lymp, membrane 0.70 3.5E−02 0.44 74 83 59 77 68 36 22 −0.70 CD231, mono, membrane 0.81 2.1E−02 0.67 81 100 63 73 100 8 8 −0.70 CD235A, total, membrane 0.94 1.7E−03 0.63 81 75 88 86 78 8 8 1.06 CD335, lymp, membrane 0.96 5.5E−02 0.73 87 88 86 88 86 8 7 −0.57 CD337, lymp, membrane 0.96 4.3E−03 0.64 80 100 57 73 100 8 7 −0.55 CD45, lymp, membrane 0.64 6.1E−02 0.25 63 54 71 59 66 54 69 0.47 CD49D, total, membrane 0.88 1.0E−02 0.61 80 75 86 86 75 8 7 1.03 CD66A/C/D/E, lymp, 0.92  7.0E−020.52 75 88 63 70 83 8 8 −0.50 membrane CD73, total, membrane 0.98 1.2E−02 0.75 86 75 100 100 75 8 6 1.05 CD84, total, membrane 0.95 5.6E−02 0.73 85 75 100 100 71 8 5 0.51 EGFR, lymp, membrane 0.95 1.3E−02 0.76 87 100 71 80 100 8 7 −1.01 GPR162, total, membrane 0.77 1.0E−03 0.39 70 70 70 79 59 37 23 0.84 HLA-A/B/C, lymp, membrane 0.84 4.3E−03 0.47 73 88 57 70 80 8 7 −0.58 HLA-A/B/C, mono, membrane 0.86 1.1E−03 0.76 87 75 100 100 78 8 7 −0.75 ITGAM, gran, membrane 0.68 8.6E−03 0.26 65 52 74 59 68 33 46 1.43 ITGAM, mean, membrane 0.67 1.1E−02 0.15 59 45 70 52 64 33 46 1.31 ITGAM, total, membrane 0.74 4.8E−04 0.37 70 58 78 66 72 33 46 1.29 NRG1, mean, membrane 0.68 3.1E−02 0.45 73 67 78 69 77 33 46 0.97 NRG1, total, membrane 0.76 1.0E−04 0.39 71 61 78 67 73 33 46 1.06 RAP1B, gran, membrane 0.66 5.4E−02 0.38 70 64 74 64 74 33 46 1.07 RAP1B, mean, membrane 0.68 2.2E−02 0.21 62 52 70 55 67 33 46 0.87 RAP1B, total, membrane 0.76 9.0E−05 0.32 67 58 74 61 71 33 46 1.17 SELI, total, membrane 0.67 7.2E−03 0.31 66 64 67 58 72 33 46 0.68 SPINT2, lymp, membrane 0.65 5.6E−02 0.28 59 85 41 51 79 33 46 −0.53 SSEA1, gran, membrane 0.95 1.6E−03 0.6 80 88 71 78 83 8 7 −0.68 SSEA1, lymp, membrane 0.84 3.1E−02 0.66 80 63 100 100 70 8 7 −1.60 ADIPOR1, gran, membrane 0.68 8.3E−03 0.34 68 60 74 64 70 47 61 1.37 ADIPOR1, mean, membrane 0.69 2.2E−03 0.37 69 62 75 66 72 47 61 1.21 ADIPOR1, total, membrane 0.77 1.5E−05 0.41 71 60 80 70 72 47 61 1.41 CD15, mean, membrane 0.67 4.2E−02 0.29 65 59 70 61 68 56 69 0.78 CD15, total, membrane 0.74 3.0E−04 0.36 69 55 80 69 69 56 69 0.86 CD8A, total, membrane 0.97 3.0E−03 0.84 92 88 100 100 80 8 4 1.85 IFITM1, lymp, membrane 0.73 2.2E−06 0.29 63 76 52 58 71 79 90 −0.64 IFITM1, mono, membrane 0.72 6.6E−06 0.32 66 72 60 61 71 79 90 −0.75 IFITM3, mono, membrane 0.56 3.1E−01 0.02 52 64 39 54 49 99 88 −0.70 IL7R, mean, membrane 0.60 1.3E−01 0.17 59 52 65 58 59 100 106 0.52 IL7R, total, membrane 0.71 5.5E−08 0.33 67 58 75 68 65 100 106 0.57 CRP*, soluble 0.89  1.2E−470.68 84 82 85 83 85 180 216 2.64 CRP, soluble 0.87  7.9E−50 0.61 81 7883 81 81 249 277 2.4 sTREM, soluble 0.67  1.2E−05 0.33 66 56 77 70 64 9698 0.55 SAA*, soluble 0.83  5.3E−33 0.53 78 77 79 75 80 177 213 1.56SAA, soluble 0.80  9.5E−39 0.50 75 71 78 74 75 244 274 1.50 ANC 0.68 1.6E−07 0.26 63 53 72 65 62 151 159 0.68 Age 0.81  0.0E+00 0.48 73 5590 84 67 179 181 3.52 Cr 0.81  6.4E−10 0.51 76 68 83 79 73 148 160 1.01K 0.70  1.1E−04 0.34 67 72 62 65 69 149 151 −0.10 Lym(%) 0.78  0.0E+000.43 71 79 63 68 75 178 179 −1.00 Neu(%) 0.76  0.0E+00 0.41 70 77 63 6874 179 180 0.39 Pulse 0.70  2.7E−09 0.34 67 68 66 63 70 141 163 −0.32Urea 0.64  1.7E−07 0.19 59 48 70 60 59 149 162 0.46 goat IgG, lymp,membrane 0.63  1.7E−01 0.27 63 78 47 60 68 83 83 −0.54 mouse   IgG1,lymp, membrane 0.91  1.0E−02 0.87 93 100 86 89 100 8 7 −1.58 mouse  IgG1, mono, membrane 1.00  1.9E−02 0.76 87 100 71 80 100 8 7 −1.48 mouse  IgG3, lymp, membrane 0.93  1.3E−02 0.87 93 100 86 89 100 8 7 −1.43*Results obtained on patients whose reference standard was determined byan expert consensus

TABLE 2B DETERMINANTS that differentiate between mixed versus viralinfected subjects Positives (P) and Negatives (N) correspond to mixed(i.e. bacterial and viral co-infections) and viral infected patientsrespectively. TA, Sen, Spe and log2(R) correspond total accuracysensitivity, specificity and log2 ratio between medians of the positiveand negative classes respectively. t-test TA Sen Spe PPV NPV DETERMINANTAUC P-value MCC % % % % % P N log2(R) ANC 0.68 4.95E−05 0.18 69 47 74 2986 36 159 0.5751 ATP6V0B, gran, intra 0.77 4.97E−03 0.3 64 81 60 28 9516 86 −0.55 ATP6V0B, lymp, intra 0.78 2.09E−03 0.26 63 75 60 26 93 16 86−0.71912 ATP6V0B, mean, intra 0.81 1.86E−03 0.39 69 88 65 33 96 16 83−0.74317 ATP6V0B, mono, intra 0.77 4.97E−03 0.3 64 81 60 28 95 16 86−0.55 B2M, Plasma 0.8 0.0008 0.44 74 63 81 67 78 16 26 −0.33734 CES1,gran, intra 0.80 7.13E−03 0.24 61 75 58 25 93 16 86 −0.87267 CES1, lymp,intra 0.78 1.07E−02 0.37 67 88 63 30 96 16 86 −0.75882 CES1, mean, intra0.81 6.88E−03 0.29 66 75 64 29 93 16 83 −0.84451 CES1, mono, intra 0.807.13E−03 0.24 61 75 58 25 93 16 86 −0.87267 CHI3L1, plasma, secreted0.70 3.05E−05 0.43 84 50 91 56 89 28 129 1.167 CMPK2, lymp, intra 0.793.33E−03 0.36 72 77 71 32 94 13 72 −0.80191 CORO1A, mean, intra 0.757.62E−04 0.27 59 81 54 27 93 21 101 −0.86925 CRP 0.92 0.00E+00 0.62 8879 89 61 95 38 179 2.7501 HERC5, lymp, intra 0.75 6.80E−02 0.28 61 81 5727 94 16 84 −0.78318 IFITM1, lymp, membrane 0.78 1.40E−02 0.22 55 81 5022 94 16 90 −1.1503 LIPT1, gran, intra 0.76 7.28E−03 0.23 60 75 57 24 9216 86 −0.44913 LIPT1, lymp, intra 0.80 6.47E−03 0.35 65 88 60 29 96 1686 −0.95089 LIPT1, mean, intra 0.75 8.21E−03 0.22 59 75 55 24 92 16 83−0.45201 LIPT1, mono, intra 0.76 7.28E−03 0.23 60 75 57 24 92 16 86−0.44913 LIPT1, mono, intra 0.84 7.77E−04 0.37 68 88 65 29 97 16 96−0.83291 LOC26010, lymp, intra 0.83 9.65E−04 0.39 71 88 68 31 97 16 96−0.90319 LOC26010, mean, intra 0.83 7.68E−04 0.37 68 88 65 30 97 16 93−0.84355 LOC26010, mono, intra 0.84 7.77E−04 0.37 68 88 65 29 97 16 96−0.83291 LRDD, lymp, intra 0.84 3.82E−03 0.48 78 88 76 39 97 8 46 −1.042Lym (%) 0.68 3.41E−04 0.14 57 62 56 24 87 39 179 −0.94237 MCP-2, serum,secreted 0.71 3.26E−03 0.22 56 77 52 27 91 30 130 −0.79708 MX1, gran,intra 0.79 2.16E−03 0.31 61 86 57 26 96 21 119 −1.2255 MX1, lymp, intra0.76 1.55E−03 0.27 61 81 57 25 94 21 119 −1.1924 MX1, mean, intra 0.771.89E−03 0.31 64 81 61 27 95 21 116 −1.1255 MX1, mono, intra 0.792.16E−03 0.31 61 86 57 26 96 21 119 −1.2255 Neu (%) 0.67 4.46E−04 0.1458 62 57 24 87 39 180 0.36 OAS2, gran, intra 0.75 4.54E−02 0.23 55 81 5122 94 21 120 −0.77111 OAS2, mono, intra 0.75 4.54E−02 0.23 55 81 51 2294 21 120 −0.77111 PARP9, gran, intra 0.77 2.40E−03 0.33 66 81 63 30 9416 81 −0.77811 PARP9, lymp, intra 0.87 2.42E−03 0.48 76 88 74 40 97 1681 −1.0077 PARP9, mono, intra 0.77 2.40E−03 0.33 66 81 63 30 94 16 81−0.77811 RSAD2, gran, intra 0.83 2.11E−04 0.34 65 86 62 29 96 21 115−1.5097 RSAD2, lymp, intra 0.75 3.01E−03 0.28 61 81 57 26 94 21 115−0.80053 RSAD2, mean, intra 0.79 4.35E−04 0.35 65 86 62 30 96 21 112−1.2099 RSAD2, mono, intra 0.83 2.11E−04 0.34 65 86 62 29 96 21 115−1.5097 SART3, lymp, intra 0.82 5.82E−03 0.36 68 82 65 32 95 11 55−1.0403 SAA, Plasma, secreted 0.90 0.00353 0.63 82 100 78 50 100 5 230.61466 TRAIL, Plasma, secreted 0.88 1.46E−06 0.49 77 83 76 45 95 30 129−1.5522 WBC 0.68 8.15E−06 0.18 67 51 71 27 87 39 180 0.44066 Mac-2BP,Plasma 0.61 0.007982 0.16 55 68 53 24 89 47 220 −0.5046 sVEGFR2, Plasma0.73 0.003814 0.32 69 71 69 36 90 34 134 −0.42652

TABLE 2C DETERMINANTS that differentiate between mixed versus bacterialinfected subjects Positives (P) and Negatives (N) correspond to mixed(i.e. bacterial and viral co-infections) and bacterial infected patientsrespectively. TA, Sen, Spe and log2(R) correspond total accuracysensitivity, specificity and log2 ratio between medians of the positiveand negative classes respectively. t-test DETERMINANT AUC P-value MCC TA% Sen % Spe % PPV % NPV % P N log2(R) BRI3BP, gran, intra 0.91 2.96E−040.37 75 71 76 36 93 7 37 −1.9632 BRI3BP, mean, intra 0.91 2.73E−04 0.477 71 78 38 94 7 37 −1.9369 BRI3BP, mono, intra 0.91 2.96E−04 0.37 75 7176 36 93 7 37 −1.9632 CES1, gran, intra 0.78 1.03E−03 0.29 64 75 61 3390 16 61 −1.0125 CES1, lymp, intra 0.78 2.40E−03 0.38 65 88 59 36 95 1661 −0.77096 CES1, mean, intra 0.79 6.48E−04 0.35 65 81 61 36 92 16 59−1.1055 CES1, mono, intra 0.78 1.03E−03 0.29 64 75 61 33 90 16 61−1.0125 Cr 0.69 6.56E−02 0.19 53 76 48 25 90 34 148 −0.87447 LOC26010,lymp, intra 0.77 1.01E−03 0.34 68 81 65 30 95 16 86 −0.78619 PARP9,lymp, intra 0.76 1.91E−03 0.38 68 81 64 39 92 16 56 −0.60984 TRIM22,gran, intra 0.80 8.44E−04 0.36 70 82 68 31 96 11 63 −0.96135 TRIM22,mean, intra 0.81 7.76E−04 0.36 70 82 68 31 96 11 63 −0.91131 TRIM22,mono, intra 0.80 8.44E−04 0.36 70 82 68 31 96 11 63 −0.96135

TABLE 2D DETERMINANTS that differentiate between bacterial or mixedversus viral infected subjects Positives (P) and Negatives (N)correspond to bacterial or mixed and viral infected patientsrespectively. TA, Sen, Spe and log2(R) correspond total accuracysensitivity, specificity and log2 ratio between medians of the positiveand negative classes respectively. t-test TA Sen Spe PPV NPV DETERMINANTAUC P-value MCC % % % % % P N log2(R) ADIPOR1, total, membrane 0.743.63E−05 0.42 71 61 80 75 68 59 61 1.38 ANC 0.69 3.39E−08 0.24 61 51 7269 56 187 159 0.62 ARG1, total, intra 0.73 1.82E−04 0.31 66 56 74 66 6541 46 0.75 AST (GOT) 0.37 5.98E−01 0.13 62 77 35 69 45 131 71 −0.54 Age0.78 0.00E+00 0.43 68 50 90 86 60 218 181 3.36 B2M, plasma, secreted0.78 6.25E−05 0.49 75 74 75 82 66 43 28 −0.28 Bili total 0.72 7.24E−030.28 60 54 79 90 34 96 29 0.82 CD15, total, membrane 0.73 4.01E−04 0.3567 56 78 73 64 71 69 0.84 CD337, lymp, membrane 0.96 1.58E−03 0.67 83100 57 79 100 11 7 −0.52 CD73, total, membrane 0.99 7.22E−03 0.78 88 82100 100 75 11 6 1.06 CD84, total, membrane 0.95 4.24E−02 0.59 81 82 8090 67 11 5 0.53 CHI3L1, plasma, secreted 0.76 1.67E−10 0.44 68 45 94 8961 142 129 1.19 CHP, total, intra 0.73 4.15E−03 0.28 64 51 76 66 64 4146 0.98 CMPK2, lymp, intra 0.73 1.63E−04 0.37 67 79 57 62 76 63 72 −0.59CORO1C, total, intra 0.71 4.22E−04 0.26 63 54 72 63 63 41 46 0.83 CRP,soluble 0.87  7.9E−50 0.61 81 78 83 81 81 249 277 2.68 Cr 0.76 5.27E−090.45 72 64 81 79 66 182 160 1.00 EIF2AK2, lymp, intra 0.78 2.06E−05 0.4374 81 61 80 64 43 23 −0.99 EIF4B, gran, intra 0.69 1.33E−03 0.17 59 7541 57 61 102 96 −0.75 EIF4B, lymp, intra 0.70 1.04E−02 0.13 57 78 33 5659 102 96 −0.67 EIF4B, mean, intra 0.67 1.85E−02 0.08 54 72 35 55 54 10093 −0.70 EIF4B, mono, intra 0.69 1.33E−03 0.17 59 75 41 57 61 102 96−0.75 Eotaxin, plasma, secreted 0.64 5.27E−06 0.23 62 69 53 63 60 148131 −0.39 GPR162, total, membrane 0.74 1.20E−03 0.41 71 69 74 83 57 4223 0.79 HLA-A/B/C, mono, 0.94 1.66E−04 0.8 89 82 100 100 78 11 7 −0.80membrane IFIT1, gran, intra 0.76 5.38E−07 0.41 76 85 54 81 62 54 24−0.63 IFIT1, lymp, intra 0.75 1.77E−07 0.44 77 89 52 80 68 54 25 −0.84IFIT1, mean, intra 0.79 1.28E−07 0.45 77 85 58 82 64 54 24 −0.92 IFIT1,mono, intra 0.76 5.38E−07 0.41 76 85 54 81 62 54 24 −0.63 IFIT3, gran,intra 0.75 1.81E−04 0.35 71 81 52 76 60 43 23 −0.63 IFIT3, mono, intra0.74 2.48E−04 0.34 71 81 52 76 60 42 23 −0.60 IFITM1, lymp, membrane0.74 1.01E−06 0.29 64 77 51 62 68 95 90 −0.73 IFITM1, mono, membrane0.70 4.13E−06 0.31 65 72 59 65 66 95 90 −0.62 IL1a, plasma, secreted0.64 1.73E−02 0.24 61 93 24 58 76 148 131 −0.06 IL7R, total, membrane0.71 1.56E−08 0.37 68 59 77 75 62 122 106 0.56 IP10, plasma, secreted0.61 7.41E−02 0.19 59 83 33 58 63 148 131 −0.78 ISG15, gran, intra 0.751.70E−05 0.45 76 86 57 79 68 43 23 −1.16 ISG15, mean, intra 0.744.72E−05 0.41 74 83 57 78 65 42 23 −0.80 ISG15, mono, intra 0.752.48E−05 0.44 75 86 57 78 68 42 23 −1.07 ITGAM, total, membrane 0.733.29E−04 0.36 68 51 83 72 66 41 46 1.26 K 0.68 1.30E−04 0.3 66 69 61 6862 183 151 −0.10 KIAA0082, gran, intra 0.65 2.33E−04 0.2 60 68 52 57 6477 84 −0.26 KIAA0082, mono, intra 0.65 2.33E−04 0.2 60 68 52 57 64 77 84−0.26 LOC26010, gran, intra 0.67 1.67E−05 0.24 62 75 49 61 64 102 96−0.45 LOC26010, mean, intra 0.65 1.32E−04 0.22 61 73 48 60 63 100 93−0.39 LOC26010, mono, intra 0.67 1.67E−05 0.24 62 75 49 61 64 102 96−0.45 Lym (%) 0.76 0.00E+00 0.41 71 77 63 72 70 217 179 −0.97 MBOAT2,total, intra 0.66 8.46E−05 0.24 62 51 72 62 62 75 83 0.57 MCP-2, plasma,secreted 0.73 2.17E−10 0.34 67 80 53 66 70 148 130 −0.90 MX1, gran,intra 0.74 7.00E−11 0.38 69 79 57 69 69 145 119 −1.00 MX1, lymp, intra0.72 1.40E−09 0.32 67 77 55 67 66 145 119 −0.70 MX1, mean, intra 0.731.10E−09 0.36 69 77 59 69 67 142 116 −0.93 MX1, mono, intra 0.748.16E−11 0.37 69 79 57 69 69 144 119 −1.00 Mac-2BP, plasma, secreted0.76 9.56E−12 0.46 73 86 58 69 79 142 129 −0.87 Mac-2BP, soluble 0.73 7.0E−19 0.35 68 71 65 65 71 243 268 −0.84 NA 0.62 3.88E−05 0.18 59 5860 63 55 190 164 −0.01 NRG1, total, membrane 0.77 3.91E−05 0.42 71 63 7872 71 41 46 1.06 Neu (%) 0.74 0.00E+00 0.36 68 74 62 70 66 218 180 0.39OAS2, gran, intra 0.67 2.55E−04 0.24 63 76 48 64 62 145 120 −0.68 OAS2,mean, intra 0.63 1.34E−02 0.18 60 73 44 61 57 142 117 −0.49 OAS2, mono,intra 0.67 2.82E−04 0.24 63 76 48 63 62 144 120 −0.67 PARP9, gran, intra0.65 5.17E−04 0.21 59 72 48 55 66 72 81 −0.43 PARP9, lymp, intra 0.702.63E−04 0.29 64 72 57 60 70 72 81 −0.53 PARP9, mono, intra 0.655.17E−04 0.21 59 72 48 55 66 72 81 −0.43 PBS_Mem_2, lymp, 0.61 6.40E−01−0.1 43 20 70 44 42 100 83 −0.54 membrane PTEN, gran, intra 0.625.00E−02 0.16 59 70 46 60 56 92 78 −0.68 Pulse 0.66 1.91E−07 0.24 62 6460 64 60 178 163 −0.25 RAP1B, total, membrane 0.77 2.25E−05 0.33 67 5974 67 67 41 46 1.17 RPL22L1, total, intra 0.74 1.11E−03 0.37 69 59 78 7167 41 45 1.29 RSAD2, gran, intra 0.81 2.22E−16 0.41 71 80 60 71 71 140115 −1.50 RSAD2, lymp, intra 0.67 3.27E−07 0.22 62 69 52 64 58 140 115−0.46 RSAD2, mean, intra 0.78 4.19E−13 0.39 70 77 61 71 69 137 112 −1.17RSAD2, mono, intra 0.81 3.33E−16 0.41 71 80 60 71 71 139 115 −1.50RSAD2, total, intra 0.65 3.12E−06 0.29 65 77 51 66 64 137 112 −0.67RTN3, total, intra 0.74 5.53E−05 0.31 66 56 74 66 65 41 46 1.03 SELI,total, membrane 0.71 9.85E−04 0.4 70 66 74 69 71 41 46 0.73 SSEA1, gran,membrane 0.94 8.39E−04 0.53 78 82 71 82 71 11 7 −0.67 SAA, soluble 0.809.50E−39 0.5 75 71 78 74 75 244 274 1.61 TRAIL, soluble 0.85  3.8E−250.52 77 78 76 74 80 245 273 −1.30 Urea 0.62 1.51E−06 0.18 58 48 69 64 54187 162 0.39 VEGFR2, plasma, secreted 0.74 2.46E−03 0.31 66 81 48 64 6836 31 −0.25 WBC 0.62 2.22E−05 0.17 57 48 68 65 52 218 180 0.29 ZBP1,total, intra 0.74 8.14E−05 0.29 65 55 74 65 65 40 46 0.83 mIgG1, mono,membrane 0.94 1.12E−02 0.64 83 91 71 83 83 11 7 −1.36 sCD62L, plasma,secreted 0.77 1.90E−05 0.38 69 67 71 78 59 43 28 −0.27 sTREM, plasma,secreted 0.69 1.90E−06 0.33 66 56 77 73 60 111 98 0.56 sTREM1, plasma,secreted 0.75 2.94E−04 0.4 68 58 82 83 56 43 28 0.38 sVEGFR2, plasma,secreted 0.74 7.05E−10 0.4 70 79 60 69 72 148 131 −0.41

TABLE 2E DETERMINANTS pairs that differentiate between bacterial ormixed versus viral infected subjects Positives (P) and Negatives (N)correspond to bacterial or mixed and viral infected patientsrespectively. TA, Sen, Spe and log2(R) correspond total accuracysensitivity, specificity and log2 ratio between medians of the positiveand negative classes respectively. Sen Spe PPV NPV DETERMINANT #1DETERMINANT #2 AUC MCC % % % % P N CRP, soluble Mac-2BP, soluble 0.910.66 83 85 83 84 243 268 CRP, soluble SAA, soluble 0.87 0.64 78 83 80 81244 274 CRP, soluble TRAIL (measured with ELISA), soluble 0.91 0.66 8482 81 85 245 273 Mac-2BP, soluble SAA, soluble 0.85 0.54 76 80 77 78 243268 Mac-2BP, soluble TRAIL (measured with ELISA), soluble 0.87 0.54 7880 78 80 243 267 SAA, soluble TRAIL (measured with ELISA), soluble 0.880.61 82 80 78 83 244 273

TABLE 2F DETERMINANTS triplets that differentiate between bacterial ormixed versus viral infected subjects Positives (P) and Negatives (N)correspond to bacterial or mixed and viral infected patientsrespectively. TA, Sen, Spe and log2(R) correspond total accuracysensitivity, specificity and log2 ratio between medians of the positiveand negative classes respectively. Sen Spe PPV NPV DETERMINANT #1DETERMINANT #2 DETERMINANT #3 AUC MCC % % % % P N CRP, soluble Mac-2BP,soluble SAA, soluble 0.91 0.66 83 84 83 85 243 268 CRP, soluble Mac-2BP,soluble TRAIL (measured with ELISA), soluble 0.93 0.71 84 88 86 86 243267 CRP, soluble SAA, soluble TRAIL (measured with ELISA), soluble 0.910.65 83 83 81 85 244 273 Mac-2BP, soluble SAA, soluble TRAIL (measuredwith ELISA), soluble 0.90 0.64 83 82 81 84 243 267

TABLE 2G DETERMINANTS that differentiate between subjects with aninfectious versus non- infectious diseases Positives (P) and Negatives(N) correspond to patients with an infectious and non-infectious diseaserespectively. TA, Sen, Spe and log2(R) correspond total accuracysensitivity, specificity and log2 ratio between medians of the positiveand negative classes respectively. t-test TA Sen Spe PPV NPV Gene SymbolAUC P-value MCC % % % % % P N log2(R) ARPC2, total, intra 0.73 4.57E−030.27 57 53 88 97 21 170 24 1.01 ATP6V0B, total, intra 0.76 4.50E−05 0.361 56 84 95 28 158 32 0.69 BCA-1, serum, secreted 0.83 3.23E−03 0.22 6969 79 98 11 277 14 1.27 CCL19-MIP3b, serum, secreted 0.84 2.40E−03 0.2672 71 86 99 13 280 14 1.30 CES1, total, intra 0.73 5.71E−04 0.16 54 5072 90 23 158 32 0.84 CMPK2, total, intra 0.87 3.35E−04 0.39 68 64 95 9928 130 19 1.06 Cr 0.68 1.22E−01 0.13 69 72 46 90 19 342 48 −0.55 Eos (%)0.73 3.56E−06 0.22 77 81 47 92 25 334 45 −2.15 HERC5, total, intra 0.732.50E−04 0.28 61 57 81 94 27 157 31 0.72 IFI6, total, intra 0.803.10E−04 0.41 70 67 89 97 33 105 19 0.90 IFIT3, gran, intra 0.741.65E−03 0.24 58 54 80 94 23 206 35 1.59 IFIT3, mean, intra 0.761.27E−03 0.23 56 53 81 95 21 203 31 1.57 IFIT3, mono, intra 0.751.61E−03 0.26 58 54 82 95 23 205 34 1.62 IFIT3, total, intra 0.811.03E−04 0.29 64 62 81 95 24 203 31 1.90 KIAA0082, total, intra 0.756.81E−05 0.3 63 59 81 94 29 156 32 0.54 LIPT1, total, intra 0.731.50E−04 0.25 59 56 78 93 26 158 32 0.75 LOC26010, total, intra 0.764.30E−05 0.33 63 59 88 97 26 193 32 0.64 LRDD, total, intra 0.864.02E−02 0.41 73 71 91 98 29 87 11 0.83 Maximal temperature 0.920.00E+00 0.55 86 86 86 98 44 397 51 0.08 MBOAT2, total, intra 0.722.99E−04 0.26 62 59 75 92 27 158 32 1.27 Mouse IgG_Intra, total, intra0.74 2.00E−02 0.33 63 59 84 95 30 157 32 0.72 MX1, gran, intra 0.761.62E−05 0.26 61 58 80 95 23 264 41 1.15 MX1, lymp, intra 0.71 2.42E−040.22 56 52 80 95 21 264 41 0.65 MX1, mean, intra 0.76 1.99E−05 0.25 6057 81 95 21 258 37 1.09 MX1, mono, intra 0.77 1.12E−05 0.28 62 59 83 9623 263 40 1.16 MX1, total, intra 0.81 5.34E−07 0.31 65 62 84 96 24 25837 1.47 OAS2, gran, intra 0.74 1.56E−04 0.24 61 59 76 94 22 265 41 0.69OAS2, mean, intra 0.74 1.44E−04 0.23 61 59 76 94 21 259 37 0.75 OAS2,mono, intra 0.74 1.54E−04 0.25 62 59 78 95 22 264 40 0.70 OAS2, total,intra 0.80 5.45E−06 0.31 66 63 84 96 24 259 37 1.24 PARP9, total, intra0.77 2.76E−04 0.33 64 60 85 96 28 148 27 0.90 PBS_Intra_2, total, intra0.76 6.27E−03 0.34 62 57 88 96 30 114 24 0.59 Pulse 0.79 6.81E−11 0.3668 66 88 97 26 341 48 0.49 QARS, total, intra 0.88 3.15E−01 0.47 74 71100 100 31 87 11 1.03 RAB13, gran, intra 0.81 2.00E−03 0.38 67 63 89 9730 105 19 0.67 RAB13, mean, intra 0.80 1.01E−03 0.36 65 60 89 97 29 10519 0.55 RAB13, mono, intra 0.81 2.00E−03 0.38 67 63 89 97 30 105 19 0.67RAB13, total, intra 0.88 2.65E−04 0.52 75 70 100 100 38 105 19 1.10RPL34, total, intra 0.92 3.33E−04 0.49 81 79 91 99 36 87 11 1.47 RSAD2,gran, intra 0.75 1.07E−04 0.31 59 55 92 98 22 255 36 1.21 RSAD2, mean,intra 0.72 3.90E−04 0.26 58 54 88 97 20 249 32 0.93 RSAD2, mono, intra0.75 1.21E−04 0.31 60 56 91 98 22 254 35 1.24 RSAD2, total, intra 0.781.65E−05 0.34 67 65 88 98 24 249 32 1.19 SART3, total, intra 0.832.72E−04 0.38 70 68 84 96 32 105 19 0.87 TRIM22, total, intra 0.801.19E−04 0.3 67 65 79 96 24 139 19 1.34 UBE2N, gran, intra 0.80 1.05E−030.35 67 63 84 96 30 104 19 0.84 UBE2N, mean, intra 0.77 9.48E−03 0.29 6462 79 94 27 104 19 0.93 UBE2N, mono, intra 0.80 1.05E−03 0.35 67 63 8496 30 104 19 0.84 UBE2N, total, intra 0.86 3.21E−05 0.52 77 74 95 99 40104 19 1.44 UBE2N, total, intra 0.76 1.08E−03 0.31 62 57 88 97 25 148 240.74 IP10 (Luminex measurments) 0.83 6.13E−05 0.17 0.8 0.79 0.79 99 17266 14 1.87 I-TAC 0.78 1.66E−04 0.33 0.7 0.72 0.65 68 69 36 34 2.25Mac-2BP 0.71 2.40E−09 0.20 0.6 0.62 0.76 95 21 560 74 0.70 CRP 0.873 00.43 71 79 83 95 51 265 70 1.69 IL1ra 0.823 3.43E−13 0.44 70 74 84 95 46265 70 0.68 IP10 (ELISA measurements) 0.816 1.38E−14 0.44 69 75 80 93 46265 70 0.92 Lym (%) 0.668 9.41E−07 0.17 0.6 63 68 93 22 555 82 Neu (%)0.628 0.000185 0.14 0.5 49 74 92 17 557 82 Pulse 0.783 5.49E−16 0.33 0.766 80 95 26 522 79 SAA 0.845 0 0.46 72 86 76 93 58 265 70 2.22 TNFR10.78 5.70E−05 0.49 0.8 72 79 788 73 36 34 TRAIL 0.655 1.49E−05 0.17 0.653 73 94 17 572 73 WBC 0.645 2.08E−05 0.16 0.6 56 73 93 19 558 82

TABLE 2H A. DETERMINANTS that differentiate between subjects with aninfectious disease versus healthy subjects; B. DETERMINANTS pairs thatdifferentiate between subjects with an infectious disease versus healthysubjects; C. DETERMINANTS triplets that differentiate between subjectswith an infectious disease versus healthy subjects Positives (P) andNegatives (N) correspond to patients with an infectious disease andhealthy subjects respectively. TA, Sen, Spe and log2(R) correspond totalaccuracy sensitivity, specificity and log2 ratio between medians of thepositive and negative classes respectively. A. t-test TA Sen Spe PPV NPVDETERMINANT AUC P-value MCC % % % % % P N log2(R) ANC 0.79 2.03E−03 0.1867 66 79 99 9 346 14 0.59066 LOC26010, 0.86 8.58E−03 0.26 73 73 88 99 12193 8 0.92663 total, intra MX1, gran, 0.79 6.38E−03 0.18 66 65 77 98 10264 13 1.0446 intra MX1, mean, 0.84 1.38E−02 0.2 69 68 88 99 8 258 81.1112 intra MX1, mono, 0.79 6.37E−03 0.18 66 65 77 98 10 263 13 1.0513intra MX1, total, 0.89 5.96E−03 0.27 74 73 100 100 10 258 8 1.3168 intraMaximal 1.00 0.00E+00 0.63 95 95 100 100 41 397 14 0.09163 temperatureCRP 0.759 0.00050258 0.24 53 54 91 97 14 265 22 −0.95832 IL1ra 0.8322.15E−05 0.321 66 77 86 97 24 265 22 −0.54337 IP10 0.844 1.94E−06 0.31767 80 82 98 26 265 22 −0.78014 SAA 0.929 1.41E−11 0.416 75 86 100 100 37265 22 −1.7295 Pulse 0.93 3.09E−04 0.25 80 80 100 100 8 341 6 0.66879 B.DETERMINANT DETERMINANT #1 #2 AUC MCC Sen % Spe % PPV % NPV % P N CRPIL1ra 0.863 0.339 80 86 99 27 265 22 CRP IP10 0.911 0.391 82 86 99 29265 22 CRP SAA 0.946 0.43 86 95 100 37 265 22 IL1ra IP10 0.879 0.348 8386 99 29 265 22 IL1ra SAA 0.93 0.484 92 95 100 49 265 22 IP10 SAA 0.9430.517 89 95 100 43 265 22 C. DETERMINANT DETERMINANT DETERMINANT Sen SpePPV NPV #1 #2 #3 AUC MCC % % % % P N CRP IL1ra IP10 0.912 0.401 84 86 9931 265 22 CRP IL1ra SAA 0.944 0.498 91 95 100 47 265 22 CRP IP10 SAA0.953 0.527 91 95 100 46 265 22 IL1ra IP10 SAA 0.942 0.517 92 91 99 49265 22

TABLE 3A DETERMINANTS pairs that differentiate between bacterial versusviral infected subjects Positives and negatives correspond to bacterialand viral infected patients respectively DETERMINANT Gene Symbol #2 AUCMCC Sen % Spe % PPV % NPV % P N CRP VEGFR2, plasma 0.96 0.8 81 97 94 8821 31 PCT, soluble SAA, soluble 0.77 0.53 68 86 80 80 47 57 CRP PCTsoluble 0.86 0.64 77 95 92 83 47 57 PCT soluble TRAIL soluble 0.88 0.5279 75 72 81 47 57 B2M, soluble PCT soluble 0.80 0.21 45 72 75 42 33 18B2M, soluble SAA, soluble 0.82 0.43 72 78 83 65 68 45 CRP*, solubleTRAIL* (measured 0.94 0.74 84 91 88 87 177 213 with ELISA), soluble CRP,soluble TRAIL (measured 0.91 0.66 84 82 81 85 245 273 with ELISA),soluble TRAIL, plasma VEGFR2, plasma 0.94 0.67 90 77 73 92 21 31 CRP*,soluble Mac-2BP*, soluble 0.93 0.72 87 87 85 89 176 208 CRP, solubleMac-2BP, soluble 0.90 0.66 83 85 83 84 243 268 B2M, Plasma CRP 0.94 0.7180 92 94 75 41 26 CRP RSAD2, mean, intra 0.93 0.73 83 89 86 88 72 95 CRPRSAD2, gran, intra 0.93 0.72 83 89 89 83 117 113 CRP IFIT1, mean, intra0.92 0.6 80 83 91 66 51 23 CRP Eotaxin, plasma 0.92 0.66 76 89 86 80 116129 CRP MCP-2, plasma 0.92 0.65 75 89 86 80 116 128 BCA-1, plasma CRP0.92 0.7 78 91 88 82 114 129 CRP Cr 0.92 0.68 80 88 86 82 147 159 CRPRSAD2, mean, intra 0.92 0.69 79 90 89 80 114 110 CRP sVEGFR2, plasma0.92 0.68 79 88 86 83 116 129 MX1, gran, intra VEGFR2, plasma 0.92 0.71100 71 71 100 12 17 MX1, mono, intra VEGFR2, plasma 0.92 0.71 100 71 71100 12 17 RSAD2, mean, intra VEGFR2, plasma 0.92 0.51 86 67 60 89 7 12TRAIL, plasma sCD62L, plasma 0.92 0.71 90 80 83 89 21 20 CMPK2, lymp,intra CRP 0.92 0.69 73 93 88 83 49 70 Cr TRAIL, plasma 0.91 0.7 89 81 8189 113 121 CRP IP10, plasma 0.91 0.66 76 89 86 80 116 129 CRP MX1, gran,intra 0.91 0.69 80 89 88 81 122 117 B2M, Plasma TRAIL, Plasma 0.91 0.7493 80 88 87 40 25 CHI3L1, plasma EIF2AK2, lymp, intra 0.91 0.75 78 100100 71 9 5 CRP MX1, mono, intra 0.91 0.69 80 89 88 81 121 117 EIF2AK2,lymp, intra sVEGFR2, plasma 0.91 0.52 85 67 79 75 26 18 Age CRP 0.910.65 77 88 86 80 177 179 EIF4B, lymp, intra IFIT1, mean, intra 0.91 0.6584 83 91 71 51 24 CHI3L1, plasma CRP 0.91 0.65 73 91 87 79 112 127 CRPPulse 0.91 0.65 73 91 87 80 139 161 CRP MX1, mean, intra 0.91 0.66 78 8887 79 119 114 CRP IFITM1, mono, 0.90 0.63 74 88 84 79 78 88 membrane CRPIL1a, plasma 0.90 0.64 73 89 86 79 116 129 CRP sTREM, plasma 0.90 0.6678 88 86 80 94 96 CRP Lym (%) 0.90 0.66 74 90 89 78 176 177 CRP MX1,lymp, intra 0.90 0.65 77 88 87 79 122 117 CRP Neu (%) 0.90 0.64 73 89 8777 177 178 Age TRAIL, plasma 0.90 0.68 86 82 81 87 117 129 CRP OAS2,gran, intra 0.90 0.62 73 88 86 76 122 118 CRP Urea 0.90 0.64 72 91 88 78148 161 CHI3L1, plasma OAS2, gran, intra 0.90 0.41 57 83 80 63 14 12CHI3L1, plasma OAS2, mono, intra 0.90 0.41 57 83 80 63 14 12 CRP K 0.900.61 72 89 86 76 148 150 CRP OAS2, mono, intra 0.90 0.62 73 88 86 76 121118 CRP RSAD2, lymp, intra 0.90 0.61 74 86 84 76 117 113 CRP sCD62L,plasma 0.90 0.68 82 86 85 83 28 28 MX1, mean, intra VEGFR2, plasma 0.900.56 92 65 65 92 12 17 Neu (%) VEGFR2, plasma 0.90 0.51 81 71 65 85 2131 ANC CRP 0.90 0.63 72 89 86 77 149 157 Cr IFIT1, mean, intra 0.90 0.6588 77 88 77 26 13 CRP IFITM1, lymp, 0.90 0.64 76 88 84 80 78 88 membraneCRP EIF2AK2, lymp, intra 0.89 0.53 76 78 85 67 37 23 CHI3L1, plasmaIFIT1, mean, intra 0.89 0.53 73 83 90 59 26 12 CRP NA 0.89 0.62 70 90 8777 151 163 EIF4B, gran, intra TRAIL, plasma 0.89 0.61 80 81 76 85 51 69EIF4B, mono, intra TRAIL, plasma 0.89 0.61 80 81 76 85 51 69 CRP EIF4B,mean, intra 0.89 0.63 71 90 87 77 83 91 EIF2AK2, lymp, intra TRAIL,plasma 0.89 0.62 85 78 85 78 26 18 EIF4B, mean, intra TRAIL, plasma 0.890.58 80 79 74 84 50 66 TRAIL, plasma sVEGFR2, plasma 0.89 0.63 88 74 7687 117 128 TRAIL, plasma Urea 0.89 0.61 85 76 76 85 113 123 EIF4B, lymp,intra TRAIL, plasma 0.89 0.59 78 81 75 84 51 69 Cr RSAD2, gran, intra0.89 0.58 78 80 79 80 91 97 EIF2AK2, lymp, intra Mac-2BP, plasma 0.890.58 81 78 84 74 26 18 IFIT1, mean, intra RSAD2, mean, intra 0.89 0.5682 75 88 67 51 24 Lym (%) VEGFR2, plasma 0.89 0.64 90 74 70 92 21 31 CrRSAD2, mono, intra 0.88 0.58 78 80 79 80 90 97 CRP EIF4B, gran, intra0.88 0.63 71 90 87 77 85 94 CRP EIF4B, mono, intra 0.88 0.63 71 90 87 7785 94 CRP EIF4B, lymp, intra 0.88 0.61 68 90 87 76 85 94 MX1, gran,intra TRAIL, plasma 0.88 0.58 84 74 74 84 77 87 IFIT1, mean, intraRSAD2, gran, intra 0.88 0.58 84 75 88 69 51 24 IFIT1, mean, intra RSAD2,mono, intra 0.88 0.58 84 75 88 69 51 24 Mac-2BP*, soluble TRAIL*(measured 0.91 0.63 85 84 81 87 176 208 with ELISA), soluble Mac-2BP,soluble TRAIL (measured 0.87 0.54 78 81 78 80 243 267 with ELISA),soluble CHI3L1, plasma MX1, gran, intra 0.88 0.62 79 83 85 77 14 12CHI3L1, plasma MX1, mono, intra 0.88 0.62 79 83 85 77 14 12 Lym (%)TRAIL, plasma 0.88 0.6 85 74 75 85 116 128 MX1, mono, intra TRAIL,plasma 0.88 0.57 84 72 73 84 76 87 Eotaxin, plasma TRAIL, plasma 0.880.56 83 73 74 82 117 128 Cr RSAD2, mean, intra 0.88 0.56 74 82 79 77 8896 K TRAIL, plasma 0.88 0.67 88 79 80 86 113 113 Neu (%) TRAIL, plasma0.88 0.61 85 75 76 85 117 128 Pulse TRAIL, plasma 0.88 0.6 84 76 75 85109 125 MX1, lymp, intra TRAIL, plasma 0.88 0.6 86 75 75 86 77 87 MX1,mean, intra TRAIL, plasma 0.88 0.62 87 75 76 86 75 84 MX1, lymp, intraVEGFR2, plasma 0.88 0.56 92 65 65 92 12 17 RSAD2, gran, intra TRAIL,plasma 0.88 0.56 82 74 74 82 72 82 CHI3L1, plasma CRP 0.88 0.65 75 89 8878 28 28 RSAD2, mono, intra TRAIL, plasma 0.88 0.56 82 74 73 82 71 82 CrEotaxin, plasma 0.87 0.6 80 80 79 81 114 123 IFITM1, lymp, membraneTRAIL, plasma 0.87 0.5 79 71 67 82 47 63 MCP-2, plasma TRAIL, plasma0.87 0.61 86 75 76 86 117 127 IP10, plasma TRAIL, plasma 0.87 0.61 85 7576 85 117 128 BCA-1, plasma TRAIL, plasma 0.87 0.59 84 74 75 84 115 128Cr MX1, lymp, intra 0.87 0.58 77 81 80 79 96 101 NA TRAIL, plasma 0.870.61 85 75 76 85 116 124 OAS2, gran, intra TRAIL, plasma 0.87 0.6 86 7575 86 77 87 CHI3L1, plasma TRAIL, plasma 0.87 0.61 85 76 76 85 113 127Cr MX1, gran, intra 0.87 0.54 74 80 78 76 96 101 Cr Mac-2BP, plasma 0.870.54 76 78 76 78 110 121 OAS2, mono, intra TRAIL, plasma 0.87 0.6 86 7575 86 76 87 RSAD2, lymp, intra VEGFR2, plasma 0.87 0.43 86 58 55 88 7 12RSAD2, mean, intra TRAIL, plasma 0.87 0.58 86 72 73 85 70 79 Cr MCP-2,plasma 0.87 0.61 79 82 80 81 114 122 Cr MX1, mono, intra 0.87 0.54 74 8078 76 95 101 Cr VEGFR2, plasma 0.87 0.6 81 80 74 86 21 30 IL1a, plasmaTRAIL, plasma 0.87 0.6 85 74 75 85 117 128 Lym (%) RSAD2, gran, intra0.87 0.6 83 76 79 81 119 114 Lym (%) RSAD2, mono, intra 0.87 0.59 83 7578 81 118 114 ANC TRAIL, plasma 0.87 0.63 86 76 77 85 115 123 Cr MX1,mean, intra 0.87 0.54 74 80 78 77 93 100 CHI3L1, plasma MX1, mean, intra0.86 0.53 71 82 83 69 14 11 Cr RSAD2, lymp, intra 0.86 0.56 73 84 80 7691 97 Lym (%) RSAD2, mean, intra 0.86 0.59 83 76 78 81 116 112 CHI3L1,plasma MX1, lymp, intra 0.86 0.55 71 83 83 71 14 12 RSAD2, lymp, intraTRAIL, plasma 0.86 0.58 85 73 73 85 72 82 ANC VEGFR2, plasma 0.86 0.5386 68 64 88 21 31 CHI3L1, plasma VEGFR2, plasma 0.86 0.62 71 90 80 84 1729 Cr EIF2AK2, lymp, intra 0.86 0.45 73 73 82 62 37 22 Cr sTREM, plasma0.86 0.59 74 85 83 76 92 91 Neu (%) sCD62L, plasma 0.86 0.54 76 79 79 7629 28 Neu (%) RSAD2, gran, intra 0.86 0.57 82 75 77 80 119 114 IFIT1,mean, intra TRAIL, plasma 0.86 0.51 85 67 85 67 26 12 Neu (%) RSAD2,mono, intra 0.86 0.57 82 75 77 80 118 114 Age Eotaxin, plasma 0.86 0.677 82 80 80 118 131 Age RSAD2, gran, intra 0.86 0.57 77 80 80 77 119 115Lym (%) sCD62L, plasma 0.86 0.5 79 71 73 77 28 28 Age RSAD2, mono, intra0.86 0.58 78 80 80 78 118 115 BCA-1, plasma EIF2AK2, lymp, intra 0.860.44 77 67 77 67 26 18 CHI3L1, plasma IFITM1, mono, 0.86 −0.1 17 71 3350 6 7 membrane Cr sVEGFR2, plasma 0.86 0.59 79 80 78 80 114 123 MX1,gran, intra sCD62L, plasma 0.86 0.61 86 75 80 82 14 12 MX1, mono, intrasCD62L, plasma 0.86 0.61 86 75 80 82 14 12 RSAD2, gran, intra sCD62L,plasma 0.86 0.54 79 75 79 75 14 12 RSAD2, gran, intra sVEGFR2, plasma0.86 0.56 79 76 74 81 73 84 RSAD2, mono, intra sCD62L, plasma 0.86 0.5479 75 79 75 14 12 Age Mac-2BP, plasma 0.86 0.55 77 78 75 79 114 129 AgeVEGFR2, plasma 0.86 0.49 71 77 68 80 21 31 RSAD2, mono, intra sVEGFR2,plasma 0.86 0.55 79 76 74 81 72 84 CHI3L1, plasma Cr 0.86 0.58 70 87 8376 110 121 Neu (%) RSAD2, mean, intra 0.86 0.58 81 77 78 80 116 112EIF2AK2, lymp, intra Neu (%) 0.85 0.54 84 70 82 73 38 23 Mac-2BP, plasmaRSAD2, gran, intra 0.85 0.57 86 71 72 85 72 82 Cr IFITM1, lymp, 0.85 0.676 84 78 83 55 74 membrane Cr IFITM1, mono, 0.85 0.56 75 81 75 81 55 74membrane IFIT1, mean, intra Pulse 0.85 0.62 88 73 85 79 25 15 Lym (%)MCP-2, plasma 0.85 0.59 85 74 75 85 117 129 TRAIL, plasma sTREM, plasma0.85 0.53 80 72 74 79 96 98 CHI3L1, plasma Mac-2BP, plasma 0.85 0.56 8175 74 82 114 129 EIF4B, lymp, intra RSAD2, gran, intra 0.85 0.49 80 6869 79 81 91 EIF4B, lymp, intra RSAD2, mono, intra 0.85 0.49 80 68 69 7981 91 IFITM1, mono, membrane TRAIL, plasma 0.85 0.54 83 71 68 85 47 63Mac-2BP, plasma RSAD2, mono, intra 0.85 0.57 86 71 72 85 71 82 AgeCHI3L1, plasma 0.85 0.49 56 90 83 70 114 129 MCP-2, plasma VEGFR2,plasma 0.85 0.5 81 70 65 84 21 30 NA VEGFR2, plasma 0.85 0.57 90 67 6691 21 30 Urea VEGFR2, plasma 0.85 0.38 62 76 65 73 21 29 CHI3L1, plasmasVEGFR2, plasma 0.85 0.56 81 75 77 79 21 20 Cr OAS2, gran, intra 0.850.55 73 82 80 76 96 101 MCP-2, plasma Neu (%) 0.85 0.59 86 72 74 85 118129 sCD62L, plasma sVEGFR2, plasma 0.85 0.66 86 80 82 84 21 20 Cr OAS2,mono, intra 0.85 0.55 73 82 79 76 95 101 EIF4B, mean, intra RSAD2, gran,intra 0.85 0.49 81 68 70 80 79 88 EIF4B, mean, intra RSAD2, mono, intra0.85 0.49 81 68 70 80 79 88 Age Lym (%) 0.85 0.52 75 78 77 76 178 179CHI3L1, plasma TRAIL, plasma 0.85 0.62 90 70 76 88 21 20 CHI3L1, plasmaRSAD2, gran, intra 0.85 0.6 76 83 80 80 72 82 EIF4B, gran, intra RSAD2,gran, intra 0.85 0.49 80 68 69 79 81 91 EIF4B, gran, intra RSAD2, mono,intra 0.85 0.49 80 68 69 79 81 91 EIF4B, mono, intra RSAD2, gran, intra0.85 0.49 80 68 69 79 81 91 EIF4B, mono, intra RSAD2, mono, intra 0.850.49 80 68 69 79 81 91 Mac-2BP, plasma sCD62L, plasma 0.85 0.52 86 65 7281 21 20 OAS2, gran, intra VEGFR2, plasma 0.85 0.48 83 65 63 85 12 17OAS2, mono, intra VEGFR2, plasma 0.85 0.48 83 65 63 85 12 17 Cr IP10,plasma 0.85 0.54 75 80 77 77 114 123 K sCD62L, plasma 0.85 0.54 78 76 7876 27 25 Age MCP-2, plasma 0.85 0.54 67 86 81 74 118 130 CHI3L1, plasmaRSAD2, mono, intra 0.85 0.58 76 82 78 80 71 82 CMPK2, lymp, intra Cr0.85 0.66 75 89 84 83 48 66 CHI3L1, plasma RSAD2, gran, intra 0.85 0.4671 75 77 69 14 12 CHI3L1, plasma RSAD2, mono, intra 0.85 0.46 71 75 7769 14 12 EIF4B, gran, intra Mac-2BP, plasma 0.85 0.57 90 68 67 90 49 69EIF4B, mono, intra Mac-2BP, plasma 0.85 0.57 90 68 67 90 49 69 IFITM1,mono, membrane RSAD2, gran, intra 0.85 0.5 81 69 69 81 78 90 IFITM1,mono, membrane RSAD2, mono, intra 0.85 0.5 81 69 69 81 78 90 MCP-2,plasma sCD62L, plasma 0.85 0.66 81 85 85 81 21 20 ANC sCD62L, plasma0.84 0.44 70 74 73 71 27 27 EIF4B, lymp, intra Mac-2BP, plasma 0.84 0.5788 70 67 89 49 69 IFITM1, lymp, membrane RSAD2, gran, intra 0.84 0.44 7964 66 78 78 90 IFITM1, lymp, membrane RSAD2, mono, intra 0.84 0.44 79 6466 78 78 90 RSAD2, mean, intra sCD62L, plasma 0.84 0.37 64 73 75 62 1411 ANC Age 0.84 0.53 69 84 80 74 151 159 CHI3L1, plasma IFITM1, mono,0.84 0.49 62 86 76 75 47 63 membrane CMPK2, lymp, intra TRAIL, plasma0.84 0.54 83 72 68 85 41 57 ANC Cr 0.84 0.53 71 81 79 74 146 150 CHI3L1,plasma MX1, gran, intra 0.84 0.58 73 84 80 78 75 87 Cr IL1a, plasma 0.840.57 75 82 79 78 114 123 Age MX1, lymp, intra 0.84 0.48 67 81 78 70 124119 Age Neu (%) 0.84 0.52 74 78 77 75 179 180 EIF2AK2, lymp, intra Lym(%) 0.84 0.47 82 65 79 68 38 23 RSAD2, mean, intra sVEGFR2, plasma 0.840.56 82 74 73 82 71 81 CHI3L1, plasma MCP-2, plasma 0.84 0.51 75 76 7477 114 127 CHI3L1, plasma MX1, mono, intra 0.84 0.57 73 84 79 78 74 87EIF4B, mean, intra Mac-2BP, plasma 0.84 0.6 90 71 69 90 48 66 AgesVEGFR2, plasma 0.84 0.51 69 82 77 74 118 131 CHI3L1, plasma CMPK2,lymp, intra 0.84 0.58 69 88 81 79 42 57 CHI3L1, plasma IFITM1, lymp,0.84 0.53 64 87 79 76 47 63 membrane Eotaxin, plasma VEGFR2, plasma 0.840.52 90 61 61 90 21 31 OAS2, gran, intra sCD62L, plasma 0.84 0.35 50 8378 59 14 12 OAS2, mono, intra sCD62L, plasma 0.84 0.35 50 83 78 59 14 12Cr Lym (%) 0.84 0.54 77 77 76 78 147 159 Mac-2BP, plasma RSAD2, mean,intra 0.84 0.53 86 67 70 84 70 79 MX1, gran, intra Mac-2BP, plasma 0.840.55 88 67 69 87 75 87 Age MX1, gran, intra 0.84 0.53 73 80 79 74 124119 Age RSAD2, mean, intra 0.84 0.49 71 79 77 72 116 112 MX1, mono,intra Mac-2BP, plasma 0.84 0.55 88 67 69 87 74 87 Age MX1, mono, intra0.84 0.54 74 80 79 75 123 119 BCA-1, plasma Cr 0.84 0.56 73 82 79 77 112123 CHI3L1, plasma EIF4B, gran, intra 0.84 0.62 65 93 86 79 49 69CHI3L1, plasma EIF4B, mean, intra 0.84 0.61 63 94 88 78 48 66 CHI3L1,plasma EIF4B, mono, intra 0.84 0.62 65 93 86 79 49 69 CHI3L1, plasmaEotaxin, plasma 0.84 0.54 72 82 78 77 114 128 Cr EIF4B, gran, intra 0.840.64 75 89 83 82 59 79 Cr EIF4B, lymp, intra 0.84 0.64 75 89 83 82 59 79Cr EIF4B, mono, intra 0.84 0.64 75 89 83 82 59 79 Age IL1a, plasma 0.840.51 58 89 83 70 118 131 EIF2AK2, lymp, intra sTREM, plasma 0.84 0.53 8172 81 72 26 18 CHI3L1, plasma MX1, lymp, intra 0.83 0.49 65 83 77 73 7587 Cr EIF4B, mean, intra 0.83 0.66 75 90 84 83 57 78 MX1, mean, intraMac-2BP, plasma 0.83 0.55 84 71 72 83 73 84 Age IP10, plasma 0.83 0.4856 89 83 69 118 131 CHI3L1, plasma EIF2AK2, lymp, intra 0.83 0.44 77 6777 67 26 18 IFIT1, mean, intra MCP-2, plasma 0.83 0.55 96 50 81 86 26 12ANC EIF2AK2, lymp, intra 0.83 0.58 84 74 84 74 38 23 CHI3L1, plasma MX1,mean, intra 0.83 0.55 73 82 78 78 73 84 EIF2AK2, lymp, intra Pulse 0.830.46 80 65 78 68 35 23 Lym (%) RSAD2, lymp, intra 0.83 0.51 79 72 75 77119 114 Age IFITM1, lymp, 0.83 0.5 63 86 79 73 79 90 membrane Age MX1,mean, intra 0.83 0.52 71 81 80 73 121 116 Age sTREM, plasma 0.83 0.51 5890 85 69 96 98 B2M, Plasma Mac-2BP, Plasma 0.82 0.59 79 80 86 71 39 25SAA*, soluble CRP*, soluble 0.91 0.68 84 84 81 86 177 213 SAA, solubleCRP, soluble 0.87 0.64 78 83 80 81 244 274 SAA*, soluble TRAIL*(measured 0.91 0.66 83 84 81 85 177 213 with ELISA), soluble SAA,soluble TRAIL (measured 0.88 0.61 82 78 78 83 244 273 with ELISA),soluble SAA, Plasma sVEGFR2 0.796 0.46 76 73 86 57 25 11 SAA*, solubleMac-2BP*, soluble 0.88 0.62 77 85 81 81 176 208 SAA, soluble Mac-2BP,soluble 0.85 0.54 76 79 77 78 243 268 *Results obtained on patientswhose reference standard was determined by an expert consensus

TABLE 3B DETERMINANTS triplets that differentiate between bacterialversus viral infected subjects DETERMINANT #1 DETERMINANT #2 DETERMINANT#3 AUC MCC Sen % Spe % PPV % NPV % P N CRP*, soluble Mac-2BP*, solubleSAA*, soluble 0.94 0.71 89 86 84 90 176 208 CRP, soluble Mac-2BP,soluble SAA, soluble 0.91 0.66 83 84 83 85 243 268 CRP*, soluble SAA*,soluble TRAIL*(ELISA), 0.95 0.73 87 87 85 89 177 213 soluble CRP,soluble SAA, soluble TRAIL(ELISA), 0.91 0.65 83 83 81 85 244 273 solubleMac-2BP*, soluble SAA*, soluble TRAIL*(ELISA), 0.92 0.73 89 86 84 90 176208 soluble Mac-2BP, soluble SAA, soluble TRAIL(ELISA), 0.90 0.64 83 8281 84 243 267 soluble CRP MX1, mean, intra Mac-2BP, plasma 0.96 0.76 8789 87 89 71 82 CRP*, soluble Mac-2BP*, soluble TRAIL*(ELISA), 0.96 0.8090 91 89 92 176 208 soluble CRP, soluble Mac-2BP, soluble TRAIL(ELISA),0.93 0.71 84 88 86 86 243 267 soluble CRP Mac-2BP, plasma RSAD2, mean,intra 0.96 0.76 85 91 89 88 68 77 CRP Cr TRAIL, plasma 0.96 0.73 84 8988 86 112 120 CRP MX1, mean, intra TRAIL, plasma 0.96 0.74 88 87 85 8973 82 CRP MX1, mean, intra sVEGFR2, plasma 0.95 0.75 85 89 88 87 74 84Age CRP TRAIL, plasma 0.95 0.72 83 89 87 85 115 127 CRP K TRAIL, plasma0.95 0.73 81 91 90 83 112 112 CRP RSAD2, mean, intra TRAIL, plasma 0.950.78 88 90 88 90 68 77 CRP RSAD2, mean, intra sVEGFR2, plasma 0.95 0.7183 89 86 85 69 79 CRP TRAIL, plasma sVEGFR2, plasma 0.95 0.73 85 88 8787 115 126 CRP MCP-2, plasma TRAIL, plasma 0.95 0.68 80 88 86 83 115 125CRP Lym (%) TRAIL, plasma 0.95 0.76 86 90 88 88 114 126 CRP Pulse TRAIL,plasma 0.95 0.74 83 90 88 86 107 123 CRP MCP-2, plasma Mac-2BP, plasma0.95 0.77 85 92 90 87 112 125 CRP Neu (%) TRAIL, plasma 0.95 0.75 85 9088 87 115 126 ANC CRP TRAIL, plasma 0.94 0.7 82 88 86 84 113 121 CRP CrMac-2BP, plasma 0.94 0.78 87 91 90 89 109 120 B2M, Plasma CRP Mac-2BP,Plasma 0.94 0.75 84 92 94 79 38 25 B2M, Plasma CRP TRAIL, Plasma 0.940.68 85 84 89 78 39 25 CHI3L1, plasma CRP TRAIL, plasma 0.94 0.7 82 8886 85 111 125 CRP Cr RSAD2, mean, intra 0.94 0.74 83 91 89 85 87 95 ANCCRP Mac-2BP, plasma 0.94 0.75 84 91 89 86 110 121 CRP Cr MCP-2, plasma0.94 0.74 82 91 89 85 113 121 CRP K Mac-2BP, plasma 0.94 0.75 83 92 9185 109 113 CRP Lym (%) Mac-2BP, plasma 0.94 0.76 85 91 90 87 111 126 CRPMac-2BP, plasma Neu (%) 0.94 0.76 85 91 90 87 112 126 Age CRP Mac-2BP,plasma 0.94 0.78 87 91 90 89 112 127 CRP Cr MX1, mean, intra 0.94 0.7180 90 88 83 92 99 CRP MCP-2, plasma MX1, mean, intra 0.94 0.69 81 88 8684 74 83 CHI3L1, plasma CRP MX1, mean, intra 0.94 0.71 82 89 87 85 71 82CRP Mac-2BP, plasma sVEGFR2, plasma 0.94 0.76 85 91 90 87 112 126 CRPMCP-2, plasma RSAD2, mean, intra 0.94 0.7 81 88 86 84 69 78 CHI3L1,plasma CRP Mac-2BP, plasma 0.94 0.77 84 92 90 87 112 127 CRP Mac-2BP,plasma Pulse 0.94 0.76 85 91 89 88 105 123 CHI3L1, plasma CRP RSAD2,mean, intra 0.94 0.72 84 88 86 86 68 77 CRP Lym (%) RSAD2, mean, intra0.93 0.72 85 87 87 85 114 110 CRP MCP-2, plasma Neu (%) 0.93 0.68 78 8987 82 116 127 CRP Neu (%) RSAD2, mean, intra 0.93 0.7 82 87 87 83 114110 CRP Cr sVEGFR2, plasma 0.93 0.71 82 89 87 84 113 122 CRP Lym (%)MCP-2, plasma 0.93 0.7 79 91 88 83 115 127 CRP K MCP-2, plasma 0.93 0.6776 90 89 79 113 113 Age CRP MCP-2, plasma 0.93 0.7 80 89 87 83 116 128CRP Cr K 0.93 0.7 80 90 88 82 143 146 CRP MCP-2, plasma sVEGFR2, plasma0.93 0.68 81 87 85 83 116 128 Cr MX1, mean, intra TRAIL, plasma 0.930.74 90 84 83 90 72 79 Cr RSAD2, mean, intra TRAIL, plasma 0.93 0.68 8781 81 87 67 75 Age CRP sVEGFR2, plasma 0.93 0.7 83 87 85 85 116 129 CRPCr Lym (%) 0.93 0.71 81 90 88 84 146 158 ANC CRP MCP-2, plasma 0.93 0.6577 88 85 80 114 122 Age CRP RSAD2, mean, intra 0.93 0.67 79 88 87 80 114110 CRP K sVEGFR2, plasma 0.93 0.69 79 89 88 81 113 114 CHI3L1, plasmaCRP Cr 0.92 0.65 77 88 85 81 109 120 CRP Lym (%) MX1, mean, intra 0.920.69 82 88 87 82 119 114 ANC Age CRP 0.92 0.69 81 89 87 83 149 157 ANCCRP Cr 0.92 0.69 79 89 88 82 145 149 CHI3L1, plasma CRP MCP-2, plasma0.92 0.68 79 89 86 82 112 125 CRP Neu (%) sVEGFR2, plasma 0.92 0.7 81 8987 84 116 128 Age CRP Lym (%) 0.92 0.73 82 91 90 83 176 177 CRP Cr Neu(%) 0.92 0.71 81 89 88 84 147 159 CRP Lym (%) sVEGFR2, plasma 0.92 0.7281 91 89 84 115 128 CRP MCP-2, plasma Pulse 0.92 0.69 78 90 88 82 108124 CRP MX1, mean, intra Neu (%) 0.92 0.65 78 87 86 79 119 114 Age CRPNeu (%) 0.92 0.69 79 89 88 81 177 178 *Results obtained on patientswhose reference standard was determined by an expert consensus

TABLE 3C DETERMINANTS pairs that differentiate between mixed versusviral infected subjects *Positives and negatives correspond to mixed andviral infected patients DETERMINANT #1 DETERMINANT #2 AUC MCC Sen % Spe% PPV % NPV % P N ATP6V0B, mean, intra CRP 0.995 0.77 93 93 70 99 15 81CRP LIPT1, lymp, intra 0.995 0.87 100 95 79 100 15 84 CES1, gran, intraCRP 0.993 0.85 93 96 82 99 15 84 CES1, mean, intra CRP 0.992 0.82 93 9578 99 15 81 CRP PARP9, lymp, intra 0.992 0.84 100 94 75 100 15 79 PARP9,lymp, intra TRAIL, plasma 0.991 0.76 100 88 65 100 15 64 CES1, gran,intra TRAIL, plasma 0.986 0.83 100 93 75 100 15 69 CES1, mean, intraTRAIL, plasma 0.986 0.78 100 89 68 100 15 66 ATP6V0B, mean, intra TRAIL,plasma 0.985 0.81 100 91 71 100 15 66 LOC26010, lymp, intra TRAIL,plasma 0.978 0.71 93 88 64 98 15 69 CRP LOC26010, lymp, intra 0.977 0.7587 95 72 98 15 94 MX1, gran, intra TRAIL, plasma 0.972 0.72 94 89 63 9918 87 MX1, mean, intra TRAIL, plasma 0.97 0.69 94 87 61 99 18 84LOC26010, gran, intra TRAIL, plasma 0.969 0.71 93 88 64 98 15 69 CRPLOC26010, gran, intra 0.968 0.66 87 90 59 98 15 94 CRP LOC26010, mean,intra 0.968 0.66 87 90 59 98 15 91 LOC26010, mean, intra TRAIL, plasma0.965 0.71 93 88 64 98 15 66 RSAD2, gran, intra TRAIL, plasma 0.964 0.7194 88 63 99 18 82 LIPT1, lymp, intra TRAIL, plasma 0.962 0.69 87 90 6597 15 69 RSAD2, mean, intra TRAIL, plasma 0.956 0.63 89 85 57 97 18 79CRP MX1, gran, intra 0.953 0.7 85 92 65 97 20 117 CRP MX1, mean, intra0.949 0.69 85 92 65 97 20 114 CRP TRAIL, plasma 0.933 0.61 83 87 59 9629 127 CRP RSAD2, mean, intra 0.924 0.76 95 92 68 99 20 110 CRP RSAD2,gran, intra 0.923 0.75 95 91 66 99 20 113 PARP9, lymp, intra RSAD2,gran, intra 0.918 0.5 88 77 42 97 16 81 B2M, Plasma CRP 0.916 0.8 88 9288 92 16 26 LOC26010, lymp, intra RSAD2, gran, intra 0.906 0.49 88 77 4097 16 91 PARP9, lymp, intra RSAD2, mean, intra 0.903 0.48 88 74 41 97 1678 CES1, mean, intra RSAD2, gran, intra 0.898 0.51 88 77 44 97 16 78ATP6V0B, mean, intra RSAD2, gran, intra 0.897 0.51 88 77 44 97 16 78CES1, gran, intra RSAD2, gran, intra 0.896 0.5 88 77 42 97 16 81 CHI3L1,plasma CRP 0.894 0.54 78 85 53 95 27 127 LOC26010, gran, intra RSAD2,gran, intra 0.894 0.47 88 75 38 97 16 91 LOC26010, mean, intra RSAD2,gran, intra 0.893 0.47 88 75 39 97 16 88 CHI3L1, plasma PARP9, lymp,intra 0.888 0.56 71 89 59 93 14 64 LOC26010, lymp, intra RSAD2, mean,intra 0.881 0.47 81 78 41 96 16 88 LIPT1, lymp, intra RSAD2, gran, intra0.878 0.44 81 75 39 95 16 81 CES1, mean, intra CHI3L1, plasma 0.876 0.6271 92 67 94 14 66 ATP6V0B, mean, intra RSAD2, mean, intra 0.874 0.48 8874 41 97 16 78 LOC26010, lymp, intra PARP9, lymp, intra 0.873 0.41 81 7236 95 16 81 CHI3L1, plasma RSAD2, gran, intra 0.873 0.46 71 83 46 93 1782 CES1, gran, intra RSAD2, mean, intra 0.873 0.39 75 74 38 94 16 78MX1, gran, intra PARP9, lymp, intra 0.87 0.59 94 80 48 98 16 81 B2M,Plasma TRAIL, Plasma 0.87 0.58 88 72 67 90 16 25 CES1, mean, intraRSAD2, mean, intra 0.869 0.4 75 76 39 94 16 78 CHI3L1, plasma LIPT1,lymp, intra 0.867 0.6 71 91 63 94 14 69 LOC26010, gran, intra RSAD2,mean, intra 0.866 0.52 88 80 44 97 16 88 LOC26010, gran, intra PARP9,lymp, intra 0.865 0.46 88 73 39 97 16 81 MX1, mean, intra PARP9, lymp,intra 0.865 0.59 94 79 48 98 16 78 ATP6V0B, mean, intra CHI3L1, plasma0.863 0.54 64 91 60 92 14 66 LOC26010, lymp, intra MX1, mean, intra0.863 0.46 88 74 37 97 16 93 ATP6V0B, mean, intra PARP9, lymp, intra0.863 0.47 88 73 40 97 16 78 CES1, gran, intra PARP9, lymp, intra 0.8630.39 81 70 35 95 16 81 CES1, mean, intra PARP9, lymp, intra 0.863 0.4181 72 37 95 16 78 LOC26010, mean, intra RSAD2, mean, intra 0.862 0.51 8878 42 97 16 88 LOC26010, lymp, intra MX1, gran, intra 0.861 0.45 88 7436 97 16 96 LOC26010, mean, intra PARP9, lymp, intra 0.861 0.47 88 73 4097 16 78 B2M, Plasma Mac-2BP, Plasma 0.749 0.51 67 84 71 81 15 25 SAA,Plasma CRP 0.882 0.51 71 81 83 68 42 31 Mac-2BP, Plasma SAA, Plasma0.831 0.61 85 76 83 79 40 29 SAA, Plasma TRAIL, Plasma 0.873 0.6 78 8386 73 40 29 SAA Plasma sVEGFR2, secreted 0.796 0.46 76 73 86 57 25 11

TABLE 3D DETERMINANTS triplets that differentiate between mixed versusviral infected subjects Positives and negatives correspond to mixed andviral infected patients respectively Sen Spe PPV NPV DETERMINANT #1DETERMINANT #2 DETERMINANT #3 AUC MCC % % % % P N CES1, gran, intra CRPTRAIL, plasma 1 0.96 100 99 93 100 14 67 CES1, mean, intra CRP TRAIL,plasma 1 0.96 100 98 93 100 14 64 ATP6V0B, mean, intra CRP TRAIL, plasma0.999 0.92 100 97 88 100 14 64 CRP LIPT1, lymp, intra PARP9, lymp, intra0.999 0.84 100 94 75 100 15 79 CRP LIPT1, lymp, intra TRAIL, plasma0.998 0.92 100 97 88 100 14 67 CRP PARP9, lymp, intra RSAD2, gran, intra0.998 0.87 100 95 79 100 15 79 CES1, gran, intra CRP PARP9, lymp, intra0.997 0.85 93 96 82 99 15 79 CES1, gran, intra CRP RSAD2, gran, intra0.997 0.88 93 97 88 99 15 79 CRP LIPT1, lymp, intra RSAD2, gran, intra0.997 0.9 100 96 83 100 15 79 CRP PARP9, lymp, intra TRAIL, plasma 0.9970.92 100 97 88 100 14 62 ATP6V0B, mean, intra CRP PARP9, lymp, intra0.996 0.86 100 95 79 100 15 76 ATP6V0B, mean, intra CRP RSAD2, gran,intra 0.996 0.86 100 95 79 100 15 76 ATP6V0B, mean, intra CRP RSAD2,mean, intra 0.996 0.84 100 93 75 100 15 76 CES1, gran, intra CRP RSAD2,mean, intra 0.996 0.88 93 97 88 99 15 76 CES1, mean, intra CRP PARP9,lymp, intra 0.996 0.82 93 95 78 99 15 76 CES1, mean, intra CRP RSAD2,gran, intra 0.996 0.88 93 97 88 99 15 76 CES1, mean, intra CRP RSAD2,mean, intra 0.996 0.85 93 96 82 99 15 76 CRP LIPT1, lymp, intra MX1,gran, intra 0.996 0.87 100 95 79 100 15 84 CRP LIPT1, lymp, intra MX1,mean, intra 0.996 0.87 100 95 79 100 15 81 CRP LIPT1, lymp, intra RSAD2,mean, intra 0.996 0.86 100 95 79 100 15 76 CRP PARP9, lymp, intra RSAD2,mean, intra 0.996 0.84 100 93 75 100 15 76 ATP6V0B, mean, intra CRP MX1,gran, intra 0.995 0.82 93 95 78 99 15 81 CES1, gran, intra CRP MX1,gran, intra 0.995 0.85 93 96 82 99 15 84 CES1, gran, intra CRP MX1,mean, intra 0.995 0.85 93 96 82 99 15 81 CES1, mean, intra CRP MX1,gran, intra 0.995 0.85 93 96 82 99 15 81 CES1, mean, intra CRP MX1,mean, intra 0.995 0.82 93 95 78 99 15 81 CRP MX1, gran, intra PARP9,lymp, intra 0.995 0.84 100 94 75 100 15 79 ATP6V0B, mean, intra CRPLIPT1, lymp, intra 0.994 0.79 93 94 74 99 15 81 CES1, gran, intra CRPLIPT1, lymp, intra 0.994 0.82 93 95 78 99 15 84 CRP LOC26010, gran,intra TRAIL, plasma 0.994 0.89 100 96 82 100 14 67 CRP LOC26010, lymp,intra TRAIL, plasma 0.994 0.92 100 97 88 100 14 67 ATP6V0B, mean, intraCES1, gran, intra CRP 0.993 0.79 93 94 74 99 15 81 ATP6V0B, mean, intraCRP MX1, mean, intra 0.993 0.77 93 93 70 99 15 81 CES1, mean, intra CRPLIPT1, lymp, intra 0.993 0.82 93 95 78 99 15 81 CRP LIPT1, lymp, intraLOC26010, lymp, intra 0.993 0.87 100 95 79 100 15 84 CRP MX1, mean,intra PARP9, lymp, intra 0.993 0.79 93 93 74 99 15 76 ATP6V0B, mean,intra CES1, mean, intra CRP 0.992 0.79 93 94 74 99 15 81 CES1, mean,intra CRP LOC26010, lymp, intra 0.992 0.82 93 95 78 99 15 81 CRPLOC26010, mean, intra TRAIL, plasma 0.992 0.89 100 95 82 100 14 64 CES1,gran, intra CES1, mean, intra CRP 0.991 0.85 93 96 82 99 15 81 CES1,gran, intra CRP LOC26010, lymp, intra 0.991 0.82 93 95 78 99 15 84 CRPLIPT1, lymp, intra LOC26010, gran, intra 0.991 0.82 93 95 78 99 15 84CRP LOC26010, lymp, intra MX1, gran, intra 0.991 0.8 93 95 74 99 15 94CRP LOC26010, lymp, intra PARP9, lymp, intra 0.991 0.82 93 95 78 99 1579 CRP LOC26010, lymp, intra RSAD2, gran, intra 0.991 0.8 93 94 74 99 1589 CES1, gran, intra CRP LOC26010, gran, intra 0.99 0.85 93 96 82 99 1584 CES1, mean, intra CRP LOC26010, mean, intra 0.99 0.82 93 95 78 99 1581 CRP LIPT1, lymp, intra LOC26010, mean, intra 0.99 0.82 93 95 78 99 1581 CRP LOC26010, lymp, intra MX1, mean, intra 0.99 0.8 93 95 74 99 15 91ATP6V0B, mean, intra CRP LOC26010, lymp, intra 0.989 0.77 93 93 70 99 1581 ATP6V0B, mean, intra CRP LOC26010, mean, intra 0.989 0.74 93 91 67 9915 81 CES1, gran, intra CRP LOC26010, mean, intra 0.989 0.82 93 95 78 9915 81 CES1, gran, intra PARP9, lymp, intra TRAIL, plasma 0.989 0.78 9392 74 98 15 64 CES1, mean, intra CRP LOC26010, gran, intra 0.989 0.82 9395 78 99 15 81 CRP LOC26010, gran, intra PARP9, lymp, intra 0.989 0.7993 94 74 99 15 79 CRP LOC26010, mean, intra PARP9, lymp, intra 0.9890.79 93 93 74 99 15 76 PARP9, lymp, intra RSAD2, gran, intra TRAIL,plasma 0.989 0.8 100 91 71 100 15 64 ATP6V0B, mean, intra CRP LOC26010,gran, intra 0.988 0.77 93 93 70 99 15 81 ATP6V0B, mean, intra PARP9,lymp, intra TRAIL, plasma 0.988 0.75 93 90 70 98 15 61 CRP LOC26010,gran, intra MX1, gran, intra 0.988 0.73 87 94 68 98 15 94 CRP LOC26010,gran, intra MX1, mean, intra 0.988 0.73 87 93 68 98 15 91 CRP LOC26010,mean, intra MX1, gran, intra 0.988 0.73 87 93 68 98 15 91 CRP LOC26010,mean, intra MX1, mean, intra 0.988 0.73 87 93 68 98 15 91 MX1, gran,intra PARP9, lymp, intra TRAIL, plasma 0.988 0.76 93 91 70 98 15 64 MX1,mean, intra PARP9, lymp, intra TRAIL, plasma 0.987 0.75 93 90 70 98 1561 PARP9, lymp, intra RSAD2, mean, intra TRAIL, plasma 0.987 0.8 100 9071 100 15 61 CES1, gran, intra CES1, mean, intra TRAIL, plasma 0.9860.81 100 91 71 100 15 66 CRP LOC26010, lymp, intra RSAD2, mean, intra0.986 0.8 93 94 74 99 15 86 ATP6V0B, mean, intra CES1, mean, intraTRAIL, plasma 0.985 0.81 100 91 71 100 15 66 CES1, mean, intra PARP9,lymp, intra TRAIL, plasma 0.985 0.83 100 92 75 100 15 61 ATP6V0B, mean,intra CES1, gran, intra TRAIL, plasma 0.984 0.83 100 92 75 100 15 66CES1, gran, intra LOC26010, lymp, intra TRAIL, plasma 0.984 0.79 93 9374 98 15 69 CES1, gran, intra MX1, gran, intra TRAIL, plasma 0.984 0.7993 93 74 98 15 69 CRP LOC26010, gran, intra RSAD2, mean, intra 0.9840.73 87 93 68 98 15 86 CES1, gran, intra LOC26010, mean, intra TRAIL,plasma 0.983 0.79 93 92 74 98 15 66 CES1, gran, intra MX1, mean, intraTRAIL, plasma 0.983 0.83 100 92 75 100 15 66 CES1, gran, intra RSAD2,gran, intra TRAIL, plasma 0.983 0.83 100 92 75 100 15 64 CES1, mean,intra LOC26010, gran, intra TRAIL, plasma 0.983 0.79 93 92 74 98 15 66CES1, mean, intra LOC26010, lymp, intra TRAIL, plasma 0.983 0.76 93 9170 98 15 66 CES1, mean, intra LOC26010, mean, intra TRAIL, plasma 0.9830.79 93 92 74 98 15 66 CES1, mean, intra MX1, gran, intra TRAIL, plasma0.983 0.83 100 92 75 100 15 66 CES1, mean, intra MX1, mean, intra TRAIL,plasma 0.983 0.83 100 92 75 100 15 66 LOC26010, lymp, intra PARP9, lymp,intra TRAIL, plasma 0.983 0.76 93 91 70 98 15 64 ATP6V0B, mean, intraMX1, mean, intra TRAIL, plasma 0.982 0.76 93 91 70 98 15 66 CES1, gran,intra LOC26010, gran, intra TRAIL, plasma 0.982 0.79 93 93 74 98 15 69CRP LOC26010, mean, intra RSAD2, mean, intra 0.982 0.73 87 93 68 98 1586 ATP6V0B, mean, intra LOC26010, lymp, intra TRAIL, plasma 0.981 0.7993 92 74 98 15 66 ATP6V0B, mean, intra MX1, gran, intra TRAIL, plasma0.981 0.76 93 91 70 98 15 66 CES1, mean, intra RSAD2, gran, intra TRAIL,plasma 0.981 0.83 100 92 75 100 15 61 ATP6V0B, mean, intra RSAD2, gran,intra TRAIL, plasma 0.98 0.78 100 89 68 100 15 61 ATP6V0B, mean, intraRSAD2, mean, intra TRAIL, plasma 0.98 0.8 100 90 71 100 15 61 CES1,gran, intra RSAD2, mean, intra TRAIL, plasma 0.98 0.83 100 92 75 100 1561 CES1, mean, intra RSAD2, mean, intra TRAIL, plasma 0.98 0.83 100 9275 100 15 61 LOC26010, gran, intra PARP9, lymp, intra TRAIL, plasma 0.980.73 93 89 67 98 15 64 CRP LOC26010, mean, intra RSAD2, gran, intra0.979 0.75 87 94 72 98 15 86 LOC26010, mean, intra PARP9, lymp, intraTRAIL, plasma 0.979 0.73 93 89 67 98 15 61 ATP6V0B, mean, intraLOC26010, gran, intra TRAIL, plasma 0.978 0.76 93 91 70 98 15 66 CES1,mean, intra LIPT1, lymp, intra TRAIL, plasma 0.978 0.73 93 89 67 98 1566 CRP LOC26010, gran, intra RSAD2, gran, intra 0.978 0.75 87 94 72 9815 89 LIPT1, lymp, intra PARP9, lymp, intra TRAIL, plasma 0.978 0.78 9392 74 98 15 64 ATP6V0B, mean, intra LOC26010, mean, intra TRAIL, plasma0.977 0.76 93 91 70 98 15 66 CES1, gran, intra LIPT1, lymp, intra TRAIL,plasma 0.977 0.79 93 93 74 98 15 69 CES1, mean, intra CHI3L1, plasmaTRAIL, plasma 0.977 0.7 86 91 67 97 14 65 LOC26010, lymp, intra RSAD2,gran, intra TRAIL, plasma 0.976 0.76 93 91 70 98 15 64 CES1, gran, intraCHI3L1, plasma TRAIL, plasma 0.975 0.73 86 93 71 97 14 68 ATP6V0B, mean,intra LIPT1, lymp, intra TRAIL, plasma 0.974 0.76 93 91 70 98 15 66ATP6V0B, mean, intra CHI3L1, plasma CRP 0.972 0.76 92 92 71 98 13 64 CRPMX1, mean, intra TRAIL, plasma 0.972 0.75 88 93 71 97 17 82 LOC26010,lymp, intra MX1, mean, intra TRAIL, plasma 0.972 0.79 93 92 74 98 15 66LOC26010, lymp, intra RSAD2, mean, intra TRAIL, plasma 0.972 0.75 93 9070 98 15 61 ATP6V0B, mean, intra CHI3L1, plasma TRAIL, plasma 0.971 0.786 91 67 97 14 65 CHI3L1, plasma LIPT1, lymp, intra TRAIL, plasma 0.9710.73 86 93 71 97 14 68 CRP LOC26010, gran, intra LOC26010, lymp, intra0.971 0.68 87 91 62 98 15 94 CRP MX1, gran, intra TRAIL, plasma 0.9710.8 88 95 79 98 17 85 CES1, gran, intra CHI3L1, plasma CRP 0.97 0.74 9291 67 98 13 67 CRP LOC26010, lymp, intra LOC26010, mean, intra 0.97 0.6887 91 62 98 15 91 LOC26010, gran, intra RSAD2, gran, intra TRAIL, plasma0.97 0.76 93 91 70 98 15 64 LOC26010, lymp, intra MX1, gran, intraTRAIL, plasma 0.97 0.79 93 93 74 98 15 69 B2M, Plasma CRP TRAIL, Plasma0.93 0.64 75 88 80 85 16 25 B2M, Plasma CRP Mac-2BP, Plasma 0.928 0.7380 92 86 88 15 25 B2M, Plasma Mac-2BP, Plasma TRAIL, Plasma 0.853 0.5480 76 67 86 15 25

TABLE 3E DETERMINANTS pairs that differentiate between infectious versusnon-infectious disease patients Positives (P) and Negatives (N)correspond to patients with an infectious and non-infectious diseaserespectively. DETERMINANT DETERMINANT #1 #2 AUC TA Sen Spe PPV NPV P NCRP IL1ra 0.908 0.791 0.84 0.84 0.95 0.58 265 70 CRP IP10 0.93 0.7970.87 0.81 0.95 0.63 265 70 CRP Lym (%) 0.847 0.814 0.824 0.74 0.958 0.37552 77 CRP Neu (%) 0.837 0.791 0.792 0.779 0.963 0.343 554 77 CRP Pulse0.879 0.852 0.857 0.811 0.969 0.448 519 74 CRP SAA 0.896 0.743 0.84 0.800.94 0.58 265 70 CRP TNFR1 0.862 0.821 0.806 0.839 0.853 0.788 36 31 CRPTRAIL 0.843 0.777 0.78 0.75 0.963 0.29 569 68 CRP WBC 0.828 0.775 0.7770.766 0.96 0.322 555 77 IL1ra IP10 0.858 0.728 0.79 0.81 0.94 0.50 26570 IL1ra Lym (%) 0.849 0.8 0.833 0.765 0.789 0.813 36 34 IL1ra Neu (%)0.827 0.786 0.806 0.765 0.784 0.788 36 34 IL1ra Pulse 0.829 0.825 0.7420.906 0.885 0.784 31 32 IL1ra SAA 0.879 0.776 0.80 0.86 0.95 0.54 265 70IL1ra TNFR1 0.821 0.786 0.778 0.794 0.8 0.771 36 34 IL1ra TRAIL 0.8350.785 0.758 0.813 0.806 0.765 33 32 IL1ra WBC 0.79 0.771 0.806 0.7350.763 0.781 36 34 IP10 Lym (%) 0.868 0.814 0.889 0.735 0.78 0.862 36 34IP10 Neu (%) 0.85 0.8 0.917 0.676 0.75 0.885 36 34 IP10 Pulse 0.86 0.8570.806 0.906 0.893 0.829 31 32 IP10 SAA 0.896 0.785 0.80 0.84 0.95 0.53265 70 IP10 TNFR1 0.847 0.8 0.833 0.765 0.789 0.813 36 34 IP10 TRAIL0.861 0.831 0.818 0.844 0.844 0.818 33 32 IP10 WBC 0.821 0.8 0.806 0.7940.806 0.794 36 34 Lym (%) Neu (%) 0.698 0.669 0.67 0.659 0.93 0.228 55582 Lym (%) Pulse 0.821 0.753 0.752 0.759 0.953 0.319 516 79 Lym (%) SAA0.871 0.794 0.788 0.838 0.972 0.354 534 74 Lym (%) TNFR1 0.827 0.7710.833 0.706 0.75 0.8 36 34 Lym (%) TRAIL 0.711 0.643 0.636 0.699 0.940.206 538 73 Lym (%) WBC 0.72 0.673 0.674 0.671 0.933 0.233 555 82 Neu(%) Pulse 0.799 0.698 0.678 0.835 0.964 0.283 518 79 Neu (%) SAA 0.8650.796 0.793 0.824 0.97 0.355 535 74 Neu (%) TNFR1 0.801 0.786 0.75 0.8240.818 0.757 36 34 Neu (%) TRAIL 0.684 0.61 0.598 0.699 0.936 0.19 540 73Neu (%) WBC 0.682 0.643 0.646 0.622 0.921 0.206 557 82 Pulse SAA 0.8710.886 0.898 0.803 0.97 0.528 501 71 Pulse TNFR1 0.799 0.825 0.871 0.7810.794 0.862 31 32 Pulse TRAIL 0.786 0.735 0.738 0.714 0.949 0.273 507 70Pulse WBC 0.793 0.727 0.717 0.797 0.959 0.3 519 79 SAA TNFR1 0.854 0.8260.861 0.788 0.816 0.839 36 33 SAA TRAIL 0.867 0.797 0.792 0.843 0.9760.335 562 70 SAA WBC 0.861 0.8 0.797 0.824 0.97 0.359 536 74 TNFR1 TRAIL0.799 0.785 0.758 0.813 0.806 0.765 33 32 TNFR1 WBC 0.801 0.757 0.7780.735 0.757 0.758 36 34 TRAIL WBC 0.708 0.718 0.726 0.658 0.94 0.245 54173

TABLE 3F DETERMINANTS triplets that differentiate between infectiousversus non-infectious disease patients Positives (P) and Negatives (N)correspond to patients with an infectious and non-infectious diseaserespectively. DETERMINANT #1 DETERMINANT #2 DETERMINANT #3 AUC TA SenSpe P N CRP IL1ra IP10 0.931 0.788 0.85 0.84 265 70 CRP IL1ra Lym (%)0.864 0.821 0.778 0.871 36 31 CRP IL1ra Neu (%) 0.872 0.821 0.806 0.83936 31 CRP IL1ra Pulse 0.859 0.9 0.871 0.931 31 29 CRP IL1ra SAA 0.920.797 0.87 0.81 265 70 CRP IL1ra TNFR1 0.866 0.836 0.861 0.806 36 31 CRPIL1ra TRAIL 0.888 0.855 0.939 0.759 33 29 CRP IL1ra WBC 0.905 0.8510.889 0.806 36 31 CRP IP10 Lym (%) 0.9 0.821 0.806 0.839 36 31 CRP IP10Neu (%) 0.9 0.836 0.833 0.839 36 31 CRP IP10 Pulse 0.889 0.9 0.903 0.89731 29 CRP IP10 SAA 0.935 0.8 0.83 0.86 265 70 CRP IP10 TNFR1 0.882 0.8210.806 0.839 36 31 CRP IP10 TRAIL 0.903 0.887 0.879 0.897 33 29 CRP IP10WBC 0.894 0.836 0.833 0.839 36 31 CRP Lym (%) Neu (%) 0.843 0.8 0.8030.779 552 77 CRP Lym (%) Pulse 0.882 0.838 0.842 0.811 513 74 CRP Lym(%) SAA 0.871 0.827 0.831 0.797 531 69 CRP Lym (%) TNFR1 0.818 0.7910.778 0.806 36 31 CRP Lym (%) TRAIL 0.86 0.746 0.731 0.868 535 68 CRPLym (%) WBC 0.846 0.738 0.728 0.805 552 77 CRP Neu (%) Pulse 0.886 0.8460.85 0.811 515 74 CRP Neu (%) SAA 0.867 0.819 0.823 0.783 532 69 CRP Neu(%) TNFR1 0.821 0.791 0.778 0.806 36 31 CRP Neu (%) TRAIL 0.857 0.7570.747 0.838 537 68 CRP Neu (%) WBC 0.84 0.721 0.709 0.805 554 77 CRPPulse SAA 0.864 0.837 0.835 0.848 498 66 CRP Pulse TNFR1 0.84 0.85 0.9030.793 31 29 CRP Pulse TRAIL 0.869 0.831 0.827 0.862 504 65 CRP Pulse WBC0.886 0.829 0.826 0.851 516 74 CRP SAA TNFR1 0.857 0.833 0.833 0.833 3630 CRP SAA TRAIL 0.869 0.817 0.819 0.8 559 65 CRP SAA WBC 0.859 0.8270.833 0.783 533 69 CRP TNFR1 TRAIL 0.853 0.806 0.758 0.862 33 29 CRPTNFR1 WBC 0.872 0.806 0.833 0.774 36 31 CRP TRAIL WBC 0.852 0.762 0.760.779 538 68 IL1ra IP10 Lym (%) 0.863 0.829 0.861 0.794 36 34 IL1ra IP10Neu (%) 0.863 0.814 0.861 0.765 36 34 IL1ra IP10 Pulse 0.88 0.905 0.8710.938 31 32 IL1ra IP10 SAA 0.899 0.8 0.79 0.89 265 70 IL1ra IP10 TNFR10.837 0.8 0.833 0.765 36 34 IL1ra IP10 TRAIL 0.879 0.862 0.848 0.875 3332 IL1ra IP10 WBC 0.835 0.829 0.861 0.794 36 34 IL1ra Lym (%) Neu (%)0.837 0.786 0.722 0.853 36 34 IL1ra Lym (%) Pulse 0.869 0.841 0.7740.906 31 32 IL1ra Lym (%) SAA 0.887 0.841 0.833 0.848 36 33 IL1ra Lym(%) TNFR1 0.826 0.771 0.778 0.765 36 34 IL1ra Lym (%) TRAIL 0.836 0.7850.697 0.875 33 32 IL1ra Lym (%) WBC 0.85 0.814 0.778 0.853 36 34 IL1raNeu (%) Pulse 0.849 0.825 0.774 0.875 31 32 IL1ra Neu (%) SAA 0.8930.855 0.889 0.818 36 33 IL1ra Neu (%) TNFR1 0.811 0.757 0.778 0.735 3634 IL1ra Neu (%) TRAIL 0.813 0.754 0.758 0.75 33 32 IL1ra Neu (%) WBC0.842 0.8 0.806 0.794 36 34 IL1ra Pulse SAA 0.864 0.903 0.903 0.903 3131 IL1ra Pulse TNFR1 0.833 0.825 0.871 0.781 31 32 IL1ra Pulse TRAIL0.837 0.847 0.828 0.867 29 30 IL1ra Pulse WBC 0.826 0.841 0.742 0.938 3132 IL1ra SAA TNFR1 0.875 0.841 0.889 0.788 36 33 IL1ra SAA TRAIL 0.8990.877 0.939 0.813 33 32 IL1ra SAA WBC 0.936 0.884 0.889 0.879 36 33IL1ra TNFR1 TRAIL 0.789 0.769 0.758 0.781 33 32 IL1ra TNFR1 WBC 0.8280.771 0.806 0.735 36 34 IL1ra TRAIL WBC 0.775 0.723 0.727 0.719 33 32IP10 Lym (%) Neu (%) 0.855 0.786 0.833 0.735 36 34 IP10 Lym (%) Pulse0.889 0.841 0.774 0.906 31 32 IP10 Lym (%) SAA 0.911 0.87 0.917 0.818 3633 IP10 Lym (%) TNFR1 0.841 0.757 0.806 0.706 36 34 IP10 Lym (%) TRAIL0.856 0.8 0.879 0.719 33 32 IP10 Lym (%) WBC 0.855 0.786 0.833 0.735 3634 IP10 Neu (%) Pulse 0.873 0.841 0.774 0.906 31 32 IP10 Neu (%) SAA0.911 0.87 0.889 0.848 36 33 IP10 Neu (%) TNFR1 0.834 0.771 0.861 0.67636 34 IP10 Neu (%) TRAIL 0.83 0.769 0.758 0.781 33 32 IP10 Neu (%) WBC0.837 0.786 0.861 0.706 36 34 IP10 Pulse SAA 0.884 0.903 0.903 0.903 3131 IP10 Pulse TNFR1 0.855 0.841 0.871 0.813 31 32 IP10 Pulse TRAIL 0.8720.864 0.828 0.9 29 30 IP10 Pulse WBC 0.845 0.841 0.806 0.875 31 32 IP10SAA TNFR1 0.885 0.826 0.778 0.879 36 33 IP10 SAA TRAIL 0.916 0.892 0.9090.875 33 32 IP10 SAA WBC 0.923 0.884 0.917 0.848 36 33 IP10 TNFR1 TRAIL0.832 0.8 0.848 0.75 33 32 IP10 TNFR1 WBC 0.86 0.786 0.694 0.882 36 34IP10 TRAIL WBC 0.803 0.769 0.848 0.688 33 32 Lym (%) Neu (%) Pulse 0.830.773 0.771 0.785 516 79 Lym (%) Neu (%) SAA 0.863 0.796 0.792 0.824 53474 Lym (%) Neu (%) TNFR1 0.827 0.771 0.75 0.794 36 34 Lym (%) Neu (%)TRAIL 0.733 0.722 0.73 0.658 538 73 Lym (%) Neu (%) WBC 0.723 0.6610.652 0.72 555 82 Lym (%) Pulse SAA 0.878 0.843 0.845 0.831 496 71 Lym(%) Pulse TNFR1 0.834 0.825 0.935 0.719 31 32 Lym (%) Pulse TRAIL 0.8050.757 0.754 0.771 501 70 Lym (%) Pulse WBC 0.826 0.79 0.802 0.709 516 79Lym (%) SAA TNFR1 0.843 0.768 0.75 0.788 36 33 Lym (%) SAA TRAIL 0.8870.836 0.841 0.8 529 70 Lym (%) SAA WBC 0.865 0.778 0.772 0.824 534 74Lym (%) TNFR1 TRAIL 0.822 0.769 0.788 0.75 33 32 Lym (%) TNFR1 WBC 0.8240.786 0.861 0.706 36 34 Lym (%) TRAIL WBC 0.746 0.722 0.727 0.685 538 73Neu (%) Pulse SAA 0.886 0.856 0.861 0.817 497 71 Neu (%) Pulse TNFR10.833 0.841 0.935 0.75 31 32 Neu (%) Pulse TRAIL 0.78 0.712 0.7 0.8 50370 Neu (%) Pulse WBC 0.796 0.737 0.734 0.759 518 79 Neu (%) SAA TNFR10.848 0.812 0.861 0.758 36 33 Neu (%) SAA TRAIL 0.885 0.803 0.798 0.843530 70 Neu (%) SAA WBC 0.862 0.783 0.781 0.797 535 74 Neu (%) TNFR1TRAIL 0.802 0.785 0.818 0.75 33 32 Neu (%) TNFR1 WBC 0.822 0.771 0.7780.765 36 34 Neu (%) TRAIL WBC 0.714 0.672 0.676 0.644 540 73 Pulse SAATNFR1 0.811 0.823 0.903 0.742 31 31 Pulse SAA TRAIL 0.865 0.878 0.8850.821 497 67 Pulse SAA WBC 0.878 0.889 0.902 0.803 498 71 Pulse TNFR1TRAIL 0.803 0.814 0.862 0.767 29 30 Pulse TNFR1 WBC 0.833 0.825 0.8710.781 31 32 Pulse TRAIL WBC 0.784 0.749 0.748 0.757 504 70 SAA TNFR1TRAIL 0.859 0.785 0.788 0.781 33 32 SAA TNFR1 WBC 0.891 0.812 0.8330.788 36 33 SAA TRAIL WBC 0.879 0.832 0.836 0.8 531 70 TNFR1 TRAIL WBC0.79 0.754 0.758 0.75 33 32

TABLE 3G DETERMINANTS quadruplets diagnostic accuracy DETERMINANTDETERMINANT DETERMINANT DETERMINANT #1 #2 #3 #4 AUC Sen % Spe % Mixedversus viral infected patients (DETERMINANT quadruplets) CRP Mac-2BP,Plasma TRAIL, Plasma sVEGFR2, Plasma 0.949 94 89 CRP Mac-2BP, PlasmaSAA, Plasma sVEGFR2, Plasma 0.909 100 82 Mac-2BP, Plasma SAA, PlasmaTRAIL, Plasma sVEGFR2, Plasma 0.864 100 73 CRP SAA, Plasma TRAIL, PlasmasVEGFR2, Plasma 0.727 100 55 CRP Mac-2BP, Plasma SAA, Plasma TRAIL,Plasma 0.63 67 89 Bacterial or Mixed versus viral infected patients(DETERMINANT quadruplets) CRP Mac-2BP, Plasma TRAIL, Plasma sVEGFR2,Plasma 0.956 93 90 CRP SAA, Plasma TRAIL, Plasma sVEGFR2, Plasma 0.94187 91 Mac-2BP, Plasma SAA, Plasma TRAIL, Plasma sVEGFR2, Plasma 0.941 9182 CRP, soluble Mac-2BP, soluble SAA, soluble TRAIL (ELISA), 0.932 85 88soluble CRP Mac-2BP, Plasma SAA, Plasma sVEGFR2, Plasma 0.893 83 82Bacterial versus viral infected patients (DETERMINANT quadruplets) CRPMac-2BP, Plasma TRAIL, Plasma sVEGFR2, Plasma 0.947 93 89 CRP SAA,Plasma TRAIL, Plasma sVEGFR2, Plasma 0.922 90 82 CRP*, soluble Mac-2BP*,SAA*, soluble TRAIL* (ELISA), 0.958 91 90 soluble soluble CRP, solubleMac-2BP, soluble SAA, soluble TRAIL (ELISA), 0.932 85 88 solubleMac-2BP, Plasma SAA, Plasma TRAIL, Plasma sVEGFR2, Plasma 0.905 86 82CRP Mac-2BP, Plasma SAA, Plasma sVEGFR2, Plasma 0.87 90 82 *Resultsobtained on patients whose reference standard was determined by anexpert consensus

TABLE 4 Baseline characteristics of bacterial and viral patients by agegroup. A, Pediatric patients; B. Adult patients. A. Pediatric patients.Bacterial Viral patients patients (n = 79) (n = 201) P-value* Age, y6.18  (4.5) 3.64 (3.9) <0.001 Gender, % Female 52 47 0.39 Male 48 53 0.4Ethnicity, % Muslim 34 35 0.8 Jewish Sephardi 31 33 0.64 JewishAshkenazy 27 24 0.67 Christian 1.2 1.4 0.9 CBC WBC, ×1000/μL 16.4  (8.5)11.1 (5.58) <0.001 Lymphocytes, % 17.9 (13.3) 33.2 (18.3) <0.001Neutrophils, % 72.7 (16)   56.2 (19.7) <0.001 ANC, ×1000/μL 12.5  (8.2)6.5 (4.48) <0.001 Clinical/Laboratory Maximal temp, ° C. 39.4  (0.74)39.1 (0.72) 0.007 Respiratory rate, 31 (13)   32 (11.9) 0.66breath/minute Pulse, beats/minute 141 (25)   137 (27.5) 0.32Auscultatory 12 14 0.67 findings, % Urea, mg/dL 19.9  (8.9) 18.8 (7.73)0.29 B. Adult patients. Bacterial Viral patients patients (n = 129) (n =41) P-value* Age, y 50.4 (19.1) 43.4 (17.5) 0.04 Gender, % Female 48 570.28 Male 52 43 0.18 Ethnicity, % Muslim 15.8 18 0.71 Jewish Sephardi 2516 0.22 Jewish Ashkenazy 52 47 0.63 Christian 0.8 4 0.09 Comorbidities,% Asthma 6.8 5 0.6 Chronic obstructive 4.5 5 0.99 pulmonary disease(COPD) Congestive heart 2.2 5 0.43 failure (CHF) Hypertension 36.1 180.03 Hyper- 2.3 5 0.43 cholesterolemia CBC WBC, ×1000/μL 10.57 (4.42)6.92 (3.17) <0.001 Lymphocytes, % 16.23 (8.42) 23.54 (13.79) <0.001Neutrophils, % 74.14 (11.36) 64.93 (15.42) <0.001 ANC, ×1000/μL 8.12(4.12) 4.27 (2.79) <0.001 Clinical/Laboratory Maximal temp, ° C. 38.80.62 38.6 0.68 0.1 Respiratory rate, 17.8 5.9 17.8 8.2 0.98breath/minute Pulse, beats/minute 94.5 15.9 93 16.4 0.61 Auscultatory33.5 26.9 0.06 findings, % Urea, mg/dL 0.2 0.4 0.1 0.3 0.13

TABLE 5 TCM-signature accuracy in diagnosing bacterial vs viralinfections in patients whose diagnosis was clear (the ‘Clear [bacterial,viral]’ cohort). Accuracy measure (95% CI) LR+  12.9 [8.0, 20.5] LR−0.108 [0.071, 0.163] DOR 119.6 [60.2, 237.5]

TABLE 6 A. Age distribution of the ‘Consensus [bacterial, viral]’cohort; B. TCM-signature accuracy in diagnosing bacterial vs viralinfections in this cohort by age group. A. Total Bacterial Viralpatients, n patients, n (%)* patients, n (%)* All ages 343 153 (45%) 190(55%) ≦18 y 219  53 (24%) 166 (76%)  >18 y 124 100 (81%)  24 (19%) B.LR+ [95% CI] LR− [95% CI] DOR [95% CI] All ages 11.8 [7.2, 19.1]  0.065[0.035, 0.122] 180.2 [76.6, 423.8] ≦18 y 9.7 [6.0, 15.5] 0.077 [0.029,0.207] 125.1 [39.9, 392.0]  >18 y 23.3 [3.3, 165.2] 0.073 [0.036, 0.150] 318.9 [37.3, 2722.1] *Of the patients in the same age group.

TABLE 7 A. Age distribution of the ‘Majority [bacterial, viral]’ cohort;B. TCM-signature accuracy in diagnosing bacterial vs viral infections inthis cohort by age group. A. Viral Total Bacterial patients, n patients,n patients, n (%)* (%)* All ages 450 208 (46%) 242 (54%) ≦18 y 280  79(28%) 201 (72%)  >18 y 170 129 (24%)  41 (76%) B. LR+ [95% CI] LR− [95%CI] DOR [95% CI] All ages 8.1 [5.6, 11.6] 0.124 [0.084, 0.182] 65.5[36.3, 118.1] ≦18 y 7.4 [5.0, 10.8] 0.138 [0.076, 0.251] 53.3 [24.2,117.2]  >18 y 11.8 [3.9, 35.1]  0.151 [0.098, 0.234] 78.0 [21.8, 279.6]*Of the patients in the same age group.

TABLE 8 Age distribution of the ‘Majority [viral, mixed]’ cohort. TotalMixed co-infected Viral patients, n patients, n (%)* patients, n (%)*All ages 276 34 (12.3%) 242 (87.7%) ≦18 y 221 20 (9.1%)  201 (91.0%) >18 y 55 14 (25.4%)  41 (74.5%) *Of the patients in the same age group.

TABLE 9 Patient cohorts used to investigate the performance of theTCM-signature in patients that were initially excluded. Total BacterialViral patients, n patients, n patient, n ‘Consensus (bacterial, viral)’cohort 343 153 190 ‘Consensus (bacterial, viral)’ 368 167 201 cohort+excluded patients with unanimous diagnosis ‘Majority (bacterial,viral)’ cohort 450 208 242 ‘Majority (bacterial, viral)’ cohort + 504238 266 excluded patients with majority diagnosis

TABLE 10A Distribution of time from symptom onset in the ‘Majority(bacterial, viral)’ cohort. Time from Total Bacterial patients, Viralpatients, symptom onset patients, n n (%)* n (%)* 0-2 days 185 71(38.4%) 114 (61.6%)  2-4 days 133 67 (50.4%) 66 (49.6%) 4-6 days 85 45(52.9%) 40 (47.1%) 6-10 days  47 25 (53.2%) 22 (46.8%) *Of the patientsin the same subgroup.

TABLE 10B Accuracy of TCM-signature across physiological systems andclinical syndromes (analysis was performed using the ‘Majority[bacterial, viral]’ cohort and therefore the reported levels of accuracyare conservative estimates of the actual accuracy). Total accuracySensitivity Specificity Total Bacterial Viral AUC [95% CI] [95% CI] [95%CI] [95% CI] patients, n patients, n patients, n Physiological SystemRespiratory 0.95 [0.92, 0.98] 0.90 [0.85, 0.95] 0.90 [0.84, 0.96] 0.89[0.83, 0.95] 241 129 112 Systemic 0.96 [0.89, 1.00] 0.96 [0.91, 1.00]0.91 [0.79, 1.00] 0.97 [0.93, 1.00] 92 23 69 Gastrointestinal 0.89[0.70, 0.99] 0.83 [0.72, 0.92] 0.87 [0.72, 1.00] 0.80 [0.67, 0.93] 63 2340 Clinical Syndromes Fever without a 0.96 [0.89, 1.00] 0.95 [0.91,1.00] 0.92 [0.73, 1.00] 0.96 [0.91, 1.00] 84 12 72 source Pneumonia 0.94[0.88 0.99] 0.87 [0.79, 0.94] 0.85 [0.76, 0.94] 0.94 [0.81, 1.00] 79 6316 Acute tonsillitis 0.94 [0.87, 1.00] 0.91 [0.82, 1.00] 0.96 [0.89,1.00] 0.81 [0.61, 1.00] 44 28 16

TABLE 10C Accuracy of TCM-signature on different pathogens (analysis wasperformed using the ‘Majority [bacterial, viral, mixed]’ cohort). Totalaccuracy Sensitivity Specificity Total Bacterial Viral Pathogen AUC [95%CI] [95% CI] [95% CI] [95% CI] patients, n patients, n patients, nViruses Influenza A/B 0.97 [0.95, 0.99] 0.96 [0.93, 0.98] 0.95 [0.93,0.98] 0.96 [0.89, 1.00] 269 242 27 Adenovirus 0.91 [0.87, 0.95] 0.85[0.81, 0.90] 0.85 [0.81, 0.90] 0.85 [0.71, 1.00] 269 242 27Parainfluenza 0.96 [0.93, 0.98] 0.92 [0.88, 0.95] 0.92 [0.88, 0.95] 0.90[0.76, 1.00] 262 242 20 1/2/3/4 Respiratory 0.97 [0.95, 0.99] 0.91 [0.870.94] 0.90 [0.86, 0.94] 1.00 [1.00, 1.00] 259 242 17 syncytial A/BEnterovirus 0.95 [0.92, 0.98] 0.88 [0.84, 0.92] 0.88 [0.84, 0.92] 0.92[0.76, 1.00] 255 242 13 Bocavirus 1/2/3/4 0.97 [0.95, 1.00] 0.94 [0.91,0.97] 0.94 [0.91, 0.97] 1.00 [1.00, 1.00] 252 242 10 Metapneumovirus0.91 [0.85, 0.97] 0.84 [0.80, 0.89] 0.84 [0.79, 0.89] 0.89 [0.63, 1.00]251 242 9 CMV 0.92 [0.86, 0.97] 0.84 [0.79, 0.88] 0.83 [0.79, 0.88] 0.89[0.63, 1.00] 251 242 9 Bacteria E. Coli 0.90 [0.82, 0.98] 0.81 [0.76,0.86] 0.89 [0.76, 1.00] 0.80 [0.75, 0.85] 269 27 242 Group A Strep 0.96[0.87, 1.00] 0.91 [0.87, 0.95] 1.00 [1.00, 1.00] 0.90 [0.87, 0.90] 25311 242 Atypical bacteria Mycoplasma pneu. 0.88 [0.78, 1.00] 0.75 [0.70,0.80] 0.86 [0.65, 1.00] 0.74 [0.69, 0.8] 256 14 242 Chlamydophila 0.96[0.82, 1.00] 0.92 [0.89, 0.96] 1.00 [1.00, 1.00] 0.92 [0.89, 0.96] 246 4242 pneu.

TABLE 10D Comparing the TCM-signature and standard laboratory parametersfor the identification of bacterial vs adenoviral infections. TotalSpecificity AUC [95% CI] accuracy [95% CI] Sensitivity [95% CI] [95% CI]TCM signature 0.91 [0.85, 0.96] 0.85 [0.78, 0.92] 0.85 [0.78, 0.93] 0.85[0.71, 1.00] ANC 0.68 [0.58, 0.79] 0.63 [0.53, 0.71] 0.57 [0.46, 0.68]0.76 [0.60, 0.92] Lym (%) 0.78 [0.70, 0.86] 0.74 [0.67, 0.82] 0.74[0.65, 0.84] 0.76 [0.60, 0.92] Maximal 0.52 [0.41, 0.64] 0.54 [0.45,0.63]  0.5 [0.38, 0.61] 0.66 [0.48, 0.84] temperature WBC 0.53 [0.41,0.65] 0.54 [0.45, 0.63] 0.51 [0.40, 0.62] 0.63 [0.45, 0.81]

TABLE 10E Comparing TCM-signature and standard laboratory parameters forthe identification of atypical bacteria. Total Specificity AUC [95% CI]accuracy [95% CI] Sensitivity [95% CI] [95% CI] TCM- 0.91 [0.83, 1.00]0.89 [0.87, 0.94] 0.76 [0.55, 0.96] 0.90 [0.86, 0.93] signature ANC 0.70[0.57, 0.83] 0.76 [0.56, 0.96] 0.63 [0.57, 0.69] 0.64 [0.59, 0.70] Lym(%) 0.73 [0.61, 0.86] 0.71 [0.50, 0.92] 0.74 [0.69, 0.80] 0.74 [0.69,0.79] Neu (%) 0.73 [0.60, 0.85] 0.67 [0.45, 0.89] 0.75 [0.69, 0.80] 0.74[0.69, 0.79] Maximal 0.52 [0.39, 0.65] 0.61 [0.55, 0.67] 0.43 [0.20,0.66] 0.63 [0.57, 0.69] temperature WBC 0.62 [0.48, 0.75] 0.71 [0.5,0.92] 0.52 [0.46, 0.58] 0.54 [0.48, 0.60]

TABLE 10F Evaluation of the sensitivity of DETERMINANTS to variouscomorbidities WS P-value (target vs Background background groups) Targetgroup group Mac- (patients with a patients without a Age TRAIL 2BP CRPcomorbidity), n comorbidity), n interval, y Bacterial/Mixed Hypertension0.27 0.34 0.57 57 49 [38, 94] Hyperlipidemia 0.26 0.18 0.81 39 55 [36,90] Obesity 0.29 0.77 0.18 21 114 [23, 87] Asthma 0.73 0.46 0.63 17 225All ages Atherosclerosis 0.44 0.42 0.95 22 91 [34, 94] Diabetes 0.370.77 0.14 17 66 [44, 80] mellitus 2 Inflammatory 0.24 0.61 0.13 9 233All ages Viral Hypertension 0.23 0.19 0.55 8 27 [38, 94] Hyperlipidemia0.512 0.16 0.91 4 21 [36, 90] Asthma 0.46 0.51 0.05 8 234 All agesDiabetes 0.34 0.49 0.08 4 14 [44, 80] mellitus 2 Non-infectiousInflammatory 0.442 0.692 0.498 7 39 All

TABLE 10G Evaluation of the sensitivity of the DETERMINANTS to varioustypes of chronic drug regimens. WS P-value (patients treated with aspecific drug Patients vs untreated patients) Patients not treated Mac-treated with with the TRAIL 2BP CRP the drug, n drug, n Age interval, yBacterial or mixed Statins 0.30 0.70 0.76 40 86 [26, 90] Diabetesrelated 0.11 0.17 0.53 28 75 [39, 87] Beta blockers 0.61 0.13 0.76 22108  [24, 106] Aspirin 0.44 0.65 0.09 32 79 [36, 96] Antacid 0.27 0.050.78 27 119  [21, 101] Inhaled corticosteroids 0.17 0.96 0.97 16 226 Allages Bronchodilators 0.84 0.77 0.76 11 231 All ages Diuretics 0.27 0.640.15 14 42 [55, 82] Viral Statins 0.26 0.12 0.35 6 35 [26, 90] Aspirin0.36 0.77 0.71 4 22 [36, 96] Antacid 0.82 0.23 0.16 5 39  [21, 101]Inhaled corticosteroids 0.68 0.78 0.21 7 235 All ages Bronchodilators0.09 0.11 0.10 7 235 All ages

TABLE 10H TCM-signature accuracy in diagnosing bacterial sepsis vs viralinfections in adult patients. Total Patients with Viral AUC Totalaccuracy Sensitivity Specificity patients bacterial sepsis patients [95%CI] [95% CI] [95% CI] [95% CI] (adults), n (adults), n (adults), n‘Consensus (adult bacterial 0.98 0.96 0.96 0.96 89 65 24 sepsis, adultviral)’ cohort [0.95, 1.00] [0.91, 1.00] [0.90, 1.00] [0.87, 1]‘Majority (adult bacterial 0.96 0.91 0.90 0.93 128 87 41 sepsis, adultviral)’ cohort [0.93, 0.99] [0.86, 0.96] [0.83, 0.97] [0.85, 1]

TABLE 11 Evaluation of the sensitivity of TCM-signature to various typesof clinical settings. Bacterial Viral Department AUC [95% CI] Patients,n patients, n patients, n ‘Consensus PED & ED 0.95 [0.90, 0.99] 201 56145 (bacterial, viral, PED 0.91 [0.84, 0.98] 157 30 127 mixed)’ cohort*ED 0.98 [0.94, 1.00] 44 26 18 Pediatrics & Internal 0.96 [0.93, 0.99]147 102 45 Pediatrics 0.95 [0.90, 1.00] 66 27 39 Internal NA NA NA NA NA‘Majority PED & ED 0.92 [0.88, 0.95] 286 110 176 (bacterial, viral, PED0.89 [0.83, 0.95] 210 59 151 mixed)’ cohort ED 0.95 [0.91, 1.00] 76 5125 Pediatrics & Internal 0.91 [0.87, 0.95] 198 132 66 Pediatrics 0.92[0.86, 0.98] 91 41 50 Internal  0.9 [0.83, 0.96] 107 91 16 *The internaldepartment ‘Consensus (bacterial, viral)’ had only a small number ofviral patients (n = 6) and was therefore excluded from this analysis.

TABLE 12 Evaluation of the sensitivity of TCM-signature to clinicalsites Total Bacterial Viral Hospital AUC [95% CI] patients, n patients,n patients, n ‘Consensus Hillel Yaffe 0.94 [0.89, 0.99] 190 44 146(bacterial, viral, Medical Center mixed)’ cohort Bnai Zion 0.94 [0.91,0.98] 158 114 44 Medical Center ‘Majority Hillel Yaffe 0.93 [0.89, 0.97]255 79 176 (bacterial, viral, Medical Center mixed)’ cohort Bnai Zion0.92 [0.89, 0.96] 229 163 66 Medical Center

TABLE 13 Prevalence of select bacterial and viral strains in patientswith infectious diseases by age groups (‘Majority [bacterial, viral,mixed]’ cohort). All ages (n = 484) Age ≦18 y (n = 300) Age >18 y (n =184) Bacterial Viral Mixed Bacterial Viral Mixed Bacterial Viral Mixed n= 208 n = 242 n = 34 n = 79 n = 201 n = 20 n = 129 n = 41 n = 14Streptococcus 34.4% 50.6% 55.9% 56.1% 54.5% 75.0% 21.1% 31.8% 28.6%pneumoniae Haemophilus 19.1% 36.2% 38.2% 37.8% 40.4% 60.0% 7.5% 15.9%28.6% influenzae Rhinovirus 4.2% 16.7% 26.5% 9.8% 18.8% 30.0% 0.8% 6.8%21.4% A/B/C

TABLE 14 TCM-signature diagnostic utility increases as the cutoffs usedfor filtering out patients with marginal responses become morestringent. Results were computed using the ‘Consensus (bacterial,viral)’ cohort. % of diagnosed patients DOR LR+ LR− 100% 145.7 12.10.083 97% 190.8 13.8 0.072 92% 268.7 16.4 0.061 89% 430.1 20.7 0.048 77%1045.4 32.3 0.031

TABLE 15 TCM-signature diagnostic utility increases as the cutoffs usedfor filtering out patients with marginal responses become morestringent. Results were computed using the ‘Majority (bacterial, viral)’cohort. % of diagnosed patients DOR LR+ LR− 100% 64.1 8.0 0.125 97% 72.78.5 0.117 93% 88.7 9.4 0.106 90% 102.2 10.1 0.099 85% 193.9 13.9 0.07273% 273.7 16.5 0.060 64% 495.3 22.3 0.045

REFERENCES

-   A. Putto, O. Meurman, and O. Ruuskanen. 1986. “C-reactive Protein in    the Differentiation of Adenoviral, Epstein-Barr Viral and    Streptococcal Tonsillitis in Children.” European Journal of    Pediatrics 145 (3).    http://www.springerlink.com/content/n1526441457905pl/.-   Akira, S., and S. Uematsu, et al. 2006. “Pathogen Recognition and    Innate Immunity.” Cell 124 (4): 783-801.-   Appenzeller C, Ammann R A, Duppenthaler A, Gorgievski-Hrisoho M, and    Aebi C. 2002. “Serum C-reactive Protein in Children with Adenovirus    Infection.” Swiss Med Wkly 132.-   Arias, C. A., and B. E. Murray. 2009. “Antibiotic-resistant Bugs in    the 21st Century—a Clinical Super-challenge.” The New England    Journal of Medicine 360 (5): 439-443.-   Bayraktaro{hacek over (g)}lu, Taner, Ahmet Sükril Aras, Selim    Aydemir, Can Davuto{hacek over (g)}lu, Yücel Ustünda{hacek over    (g)}, Hulusi Atmaca, and Ali Borazan. 2004. “Serum Levels of Tumor    Necrosis Factor-alpha, Interleukin-6 and Interleukin-8 Are Not    Increased in Dyspeptic Patients with Helicobacter Pylori-associated    Gastritis.” Mediators of Inflammation 13 (1) (February): 25-28. doi:    10.1080/09629350410001664789.-   Bone, R C, R A Balk, F B Cerra, R P Dellinger, A M Fein, W A Knaus,    R M Schein, and W J Sibbald. 1992. “Definitions for Sepsis and Organ    Failure and Guidelines for the Use of Innovative Therapies in    Sepsis. The ACCP/SCCM Consensus Conference Committee. American    College of Chest Physicians/Society of Critical Care Medicine.”    Chest 101 (6) (June): 1644-1655.-   Bossuyt, Patrick M, Johannes B Reitsma, David E Bruns, Constantine A    Gatsonis, Paul P Glasziou, Les M Irwig, David Moher, Drummond    Rennie, Henrica C. W De Vet, and Jeroen G Lijmer. 2003. “The STARD    Statement for Reporting Studies of Diagnostic Accuracy: Explanation    and Elaboration.” Annals of Internal Medicine 138 (1) (January 7):    W1-W12.-   Brian Clyne, and Jonathan S Olshaker. 1999. “The C-reactive    Protein.” The Journal of Emergency Medicine 17 (6): 1019-1025. doi:    10.1016/S0736-4679(99)00135-3.-   Brunkhorst, F. M., and B. Al-Nawas, et al. 2002. “Procalcitonin,    C-reactive Protein and APACHE II Score for Risk Evaluation in    Patients with Severe Pneumonia.” Clinical Microbiology and    Infection: The Official Publication of the European Society of    Clinical Microbiology and Infectious Diseases 8 (2): 93-100.-   Cadieux, G., and R. Tamblyn, et al. 2007. “Predictors of    Inappropriate Antibiotic Prescribing Among Primary Care Physicians.”    CMAJ: Canadian Medical Association Journal=Journal De l'Association    Medicale Canadienne 177 (8): 877-883.-   Cao, Wenjun, Bill Sun, Mark A Feitelson, Tong Wu, Ran Tur-Kaspa, and    Qishi Fan. 2009. “Hepatitis C Virus Targets Over-expression of    Arginase I in Hepatocarcinogenesis.” International Journal of    Cancer. Journal International Du Cancer 124 (12) (June 15):    2886-2892. doi: 10.1002/ijc.24265.-   “CDC—About Antimicrobial Resistance.” 2011.    http://www.cdc.gov/drugresistance/about.html.-   “CDC—Get Smart: Fast Facts About Antibiotic Resistance.” 2011.    http://www.cdc.gov/getsmart/antibiotic-use/fast-facts.html.-   CDC.gov. 2012. “NAMCS/NHAMCS—NCHS Reports Using Ambulatory Health    Care Data.” Accessed June 6.    http://www.cdc.gov/nchs/ahcd/ahcd_reports.htm.-   Christensen, R D, P P Bradley, and G Rothstein. 1981. “The Leukocyte    Left Shift in Clinical and Experimental Neonatal Sepsis.” The    Journal of Pediatrics 98 (1) (January): 101-105.-   Cillóniz, Catia, Santiago Ewig, Eva Polverino, Maria Angeles Marcos,    Cristina Esquinas, Albert Gabarrús, Josep Mensa, and Antoni    Torres. 2011. “Microbial Aetiology of Community-Acquired Pneumonia    and Its Relation to Severity.” Thorax 66 (4) (April 1): 340-346.    doi:10.1136/thx.2010.143982.-   Clements, Helena, Terence Stephenson, Vanessa Gabriel, Timothy    Harrison, Michael Millar, Alan Smyth, William Tong, and Chris J    Linton. 2000. “Rationalised Prescribing for Community Acquired    Pneumonia: A Closed Loop Audit.” Archives of Disease in Childhood    83 (4) (October 1): 320-324. doi: 10.1136/adc.83.4.320.-   Craig, Jonathan C, Gabrielle J Williams, Mike Jones, Miriam    Codarini, Petra Macaskill, Andrew Hayen, Les Irwig, Dominic A    Fitzgerald, David Isaacs, and Mary McCaskill. 2010. “The Accuracy of    Clinical Symptoms and Signs for the Diagnosis of Serious Bacterial    Infection in Young Febrile Children: Prospective Cohort Study of 15    781 Febrile Illnesses.” BMJ: British Medical Journal 340. doi:    10.1136/bmj.c1594.-   Davey, P., and E. Brown, et al. 2006. “Systematic Review of    Antimicrobial Drug Prescribing in Hospitals.” Emerging Infectious    Diseases 12 (2): 211-216.-   Fjaertoft, G., and T. Foucard, et al. 2005. “Human Neutrophil    Lipocalin (HNL) as a Diagnostic Tool in Children with Acute    Infections: a Study of the Kinetics.” Acta Paediatrica (Oslo,    Norway: 1992) 94 (6): 661-666.-   Gobert, Alain P, Yulan Cheng, Jian-Ying Wang, Jean-Luc Boucher,    Ramaswamy K Iyer, Stephen D Cederbaum, Robert A Casero Jr, Jamie C    Newton, and Keith T Wilson. 2002. “Helicobacter Pylori Induces    Macrophage Apoptosis by Activation of Arginase II.” Journal of    Immunology (Baltimore, Md.: 1950) 168 (9) (May 1): 4692-4700.-   Gong, Jh, H Sprenger, F Hinder, A Bender, A Schmidt, S Horch, M    Nain, and D Gemsa. 1991. “Influenza A Virus Infection of    Macrophages. Enhanced Tumor Necrosis Factor-alpha (TNF-alpha) Gene    Expression and Lipopolysaccharide—Triggered TNF-alpha Release.” The    Journal of Immunology 147 (10) (November 15): 3507-3513.-   Grassmé, Heike, Susanne Kirschnek, Joachim Riethmueller, Andrea    Riehle, Gabriele von Kürthy, Florian Lang, Michael Weller, and Erich    Gulbins. 2000. “CD95/CD95 Ligand Interactions on Epithelial Cells in    Host Defense to Pseudomonas Aeruginosa.” Science 290 (5491) (October    20): 527-530. doi: 10.1126/science.290.5491.527.-   Hatherill, M., and S. M. Tibby, et al. 1999. “Diagnostic Markers of    Infection: Comparison of Procalcitonin with C Reactive Protein and    Leucocyte Count.” Archives of Disease in Childhood 81 (5): 417-421.-   Hatipo{hacek over (g)}lu, Nevin, Ayper Somer, Selim Badur, Emin    Unüvar, Meral Akçay-Ciblak, Ensar Yekeler, Nuran Salman, Melike    Keser, Hüsem Hatipo{hacek over (g)}lu, and Rengin Siraneci. 2011.    “Viral Etiology in Hospitalized Children with Acute Lower    Respiratory Tract Infection.” The Turkish Journal of Pediatrics    53 (5) (October): 508-516.-   Hersh, Adam L, Daniel J Shapiro, Andrew T Pavia, and Samir S    Shah. 2011. “Antibiotic Prescribing in Ambulatory Pediatrics in the    United States.” Pediatrics 128 (6) (December): 1053-1061.    doi:10.1542/peds.2011-1337.-   Houck, P. M., and D. W. Bratzler, et al. 2002. “Pneumonia Treatment    Process and Quality.” Archives of Internal Medicine 162 (7):    843-844.-   Johansson, Niclas, Mats Kalin, Annika Tiveljung-Lindell, Christian G    Giske, and Jonas Hedlund. 2010. “Etiology of Community-acquired    Pneumonia: Increased Microbiological Yield with New Diagnostic    Methods.” Clinical Infectious Diseases: An Official Publication of    the Infectious Diseases Society of America 50 (2) (January 15):    202-209. doi: 10.1086/648678.-   Johnstone, Jennie, Sumit R Majumdar, Julie D Fox, and Thomas J    Marrie. 2008. “Viral Infection in Adults Hospitalized With    Community-Acquired Pneumonia Prevalence, Pathogens, and    Presentation.” Chest 134 (6) (December 1): 1141-1148. doi:    10.1378/chest.08-0888.-   Jones, A. E., and J. F. Fiechtl, et al. 2007. “Procalcitonin Test in    the Diagnosis of Bacteremia: a Meta-analysis.” Annals of Emergency    Medicine 50 (1): 34-41.-   Kunze, Wolfgang, Dietmar Beier, and Katrin Groeger. 2010.    “Adenovirus Respiratory Infections In Children. Do They Mimic    Bacterial Infections?” (October 31).    http://www.webmedcentral.com/article_view/1098.-   Levy, Mitchell M, Mitchell P Fink, John C Marshall, Edward Abraham,    Derek Angus, Deborah Cook, Jonathan Cohen, Steven M Opal, Jean-Louis    Vincent, and Graham Ramsay. 2003. “2001 SCCM/ESICM/ACCP/ATS/SIS    International Sepsis Definitions Conference.” Critical Care Medicine    31 (4) (April): 1250-1256. doi: 10. 1097/01.CCM.0000050454.01978.3B.-   Linder, J. A., and R. S. Stafford. 2001. “Antibiotic Treatment of    Adults with Sore Throat by Community Primary Care Physicians: a    National Survey, 1989-1999.” JAMA: The Journal of the American    Medical Association 286 (10): 1181-1186.-   Little, P. 2005. “Delayed Prescribing of Antibiotics for Upper    Respiratory Tract Infection.” BMJ (Clinical Research Ed.) 331    (7512): 301-302.-   Little, P. S., and I. Williamson. 1994. “Are Antibiotics Appropriate    for Sore Throats? Costs Outweigh the Benefits.” BMJ (Clinical    Research Ed.) 309 (6960): 1010-1011.-   Del Mar, C. 1992. “Managing Sore Throat: a Literature Review. 1.    Making the Diagnosis.” The Medical Journal of Australia 156 (8):    572-575.-   Marc, E, M Chaussain, F Moulin, J L Iniguez, G Kalifa, J Raymond,    and D Gendrel. 2000. “Reduced Lung Diffusion Capacity After    Mycoplasma Pneumoniae Pneumonia.” The Pediatric Infectious Disease    Journal 19 (8) (August): 706-710.-   Murphy, K., and P. Travers, et al. 2007. “Janeway's Immunobiology,    Seventh Edition|Mendeley.”    http://www.mendeley.com/research/janeways-immunobiology-seventh-edition-immunobiology-immune-system-janeway/.-   Peltola, Ville, Jussi Mertsola, and Olli Ruuskanen. 2006.    “Comparison of Total White Blood Cell Count and Serum C-reactive    Protein Levels in Confirmed Bacterial and Viral Infections.” The    Journal of Pediatrics 149 (5) (November): 721-724. doi:    10.1016/j.jpeds.2006.08.051.-   Pickering, Larry K., and Herbert L. DuPont. 1986. Infectious    Diseases of Children and Adults: a Step-by-step Approach to    Diagnosis and Treatment. Addison-Wesley, Health Sciences Division.-   Povoa P. 2002. “C-reactive Protein: a Valuable Marker of Sepsis.”    Intensive Care Medicine 28 (3): 235-243.-   Principi, N, and S Esposito. 2001. “Emerging Role of Mycoplasma    Pneumoniae and Chlamydia Pneumoniae in Paediatric Respiratory-tract    Infections.” The Lancet Infectious Diseases 1 (5) (December):    334-344. doi: 10.1016/S1473-3099(01)00147-5.-   Pulcini, C., and E. Cua, et al. 2007. “Antibiotic Misuse: a    Prospective Clinical Audit in a French University Hospital.”    European Journal of Clinical Microbiology & Infectious Diseases:    Official Publication of the European Society of Clinical    Microbiology 26 (4): 277-280.-   Regev-Yochay, Gili, Izzeldin Abullaish, Richard Malley, Bracha    Shainberg, Miriam Varon, Yulia Roytman, Arnona Ziv, et al. 2012.    “Streptococcus Pneumoniae Carriage in the Gaza Strip.” PloS One 7    (4): e35061. doi:10.1371/journal.pone.0035061.-   Regev-Yochay, Gili, Meir Raz, Ron Dagan, Nurith Porat, Bracha    Shainberg, Erica Pinco, Nathan Keller, and Ethan Rubinstein. 2004.    “Nasopharyngeal Carriage of Streptococcus Pneumoniae by Adults and    Children in Community and Family Settings.” Clinical Infectious    Diseases 38 (5) (March 1): 632-639. doi:10.1086/381547.-   Restrepo, Marcos I, Eric M Mortensen, Jose A Velez, Christopher    Frei, and Antonio Anzueto. 2008. “A Comparative Study of    Community-Acquired Pneumonia Patients Admitted to the Ward and the    ICU*.” Chest 133 (3) (March 1): 610-617. doi:10.1378/chest.07-1456.-   Rivers, E, B Nguyen, S Havstad, J Ressler, A Muzzin, B Knoblich, E    Peterson, and M Tomlanovich. 2001. “Early Goal-directed Therapy in    the Treatment of Severe Sepsis and Septic Shock.” The New England    Journal of Medicine 345 (19) (November 8): 1368-1377. doi:    10.1056/NEJMoa010307.-   Rudensky, B., and G. Sirota, et al. 2008. “Neutrophil CD64    Expression as a Diagnostic Marker of Bacterial Infection in Febrile    Children Presenting to a Hospital Emergency Department.” Pediatric    Emergency Care 24 (11): 745-748.-   Scott, J. G., and D. Cohen, et al. 2001. “Antibiotic Use in Acute    Respiratory Infections and the Ways Patients Pressure Physicians for    a Prescription.” The Journal of Family Practice 50 (10): 853-858.-   Shaikh, Nader, Erica Leonard, and Judith M Martin. 2010. “Prevalence    of Streptococcal Pharyngitis and Streptococcal Carriage in Children:    a Meta-analysis.” Pediatrics 126 (3) (September): e557-564. doi:    10.1542/peds.2009-2648.-   Shapiro, Howard. 2005. Practical Flow Cytometry.    http://onlinelibrary.wiley.com/doi/10.1002/0471722731.fmatter/summary.-   Shibli, Fahmi, Bibiana Chazan, Orna Nitzan, Edit Flatau, Hana    Edelstein, Orna Blondheim, Raul Raz, and Raul Colodner. 2010.    “Etiology of Community-acquired Pneumonia in Hospitalized Patients    in Northern Israel.” The Israel Medical Association Journal: IMAJ    12 (8) (August): 477-482.-   Song, Jae-Hoon, Won Sup Oh, Cheol-In Kang, Doo Ryeon Chung, Kyong    Ran Peck, Kwan Soo Ko, Joon Sup Yeom, et al. 2008. “Epidemiology and    Clinical Outcomes of Community-acquired Pneumonia in Adult Patients    in Asian Countries: a Prospective Study by the Asian Network for    Surveillance of Resistant Pathogens.” International Journal of    Antimicrobial Agents 31 (2) (February): 107-114. doi:    10.1016/j.ijantimicag.2007.09.014.-   Spiro, D. M., and K. Y. Tay, et al. 2006. “Wait-and-see Prescription    for the Treatment of Acute Otitis Media: a Randomized Controlled    Trial.” JAMA: The Journal of the American Medical Association 296    (10): 1235-1241.-   Tang M. P., and Eslick G D. 2007. “Accuracy of Procalcitonin for    Sepsis Diagnosis in Critically III Patients: Systematic Review and    Meta-analysis.” The Lancet Infectious Diseases 7 (3): 210-217.-   Thorn, George W., Adams, Braunwald, Isselbacher, and Petersdorf.    1977. Harrison's Principles of Internal Medicine. 8th Edition.-   Uyeki, Timothy M, Ramakrishna Prasad, Charles Vukotich, Samuel    Stebbins, Charles R Rinaldo, Yu-Hui Ferng, Stephen S Morse, et    al. 2009. “Low Sensitivity of Rapid Diagnostic Test for Influenza.”    Clinical Infectious Diseases: An Official Publication of the    Infectious Diseases Society of America 48 (9) (May 1): e89-92.    doi:10.1086/597828.-   Vaneechoutte, M, G Verschraegen, G Claeys, B Weise, and A M Van den    Abeele. 1990. “Respiratory Tract Carrier Rates of Moraxella    (Branhamella) Catarrhalis in Adults and Children and Interpretation    of the Isolation of M. Catarrhalis from Sputum.” Journal of Clinical    Microbiology 28 (12) (December): 2674-2680.-   Wirth U., and Muller D. 2002. “Post-translational Modification    Detection Using Metastable Ions in Reflector Matrix-assisted Laser    Desorption/ionization-time of Flight Mass Spectrometry.” Proteomics    2 (10): 1445-1451.-   Zhou, Chao, Feng-Zhen Ma, Xue-Jie Deng, Hong Yuan, and Hong-Sheng    Ma. 2008. “Lactobacilli Inhibit Interleukin-8 Production Induced by    Helicobacter Pylori Lipopolysaccharide-activated Toll-like Receptor    4.” World Journal of Gastroenterology: WJG 14 (32) (August 28):    5090-5095. doi:10.3748/wjg. 14.5090.

We claim:
 1. A kit comprising a detection assay comprising polypeptidedetection reagents comprising labeled monoclonal antibodies immobilizedon a solid support, wherein said monoclonal antibodies comprise a firstmonoclonal antibody that specifically binds to TRAIL polypeptide, asecond monoclonal antibody that specifically binds to CRP polypeptideand a third monoclonal antibody that specifically binds to IP10polypeptide when contacted to a whole blood sample or a fraction thereoffrom a subject, wherein the detection assay further contains antibodiesthat bind to no more than two additional polypeptides which aredifferentially expressed in a statistically significant manner insubjects with a bacterial infection compared to subjects with a viralinfection.
 2. The kit of claim 1, further comprising: (i) an antibodythat specifically binds to said TRAIL polypeptide at an additional siteas to the site to which said first monoclonal antibody binds; (ii) anantibody that specifically binds to said CRP polypeptide at anadditional site as to the site to which said second monoclonal antibodybinds; and (iii) an antibody that specifically binds to said IP10polypeptide at an additional site as to the site to which said secondmonoclonal antibody binds.
 3. The kit of claim 2, wherein each of saidantibodies of (i), (ii) and (iii) comprise a detectable label selectedfrom the group consisting of a radioactive label, a fluorescent label, achemiluminescent label and an enzyme.
 4. The kit of claim 3, whereinsaid enzyme is horseradish peroxidase or alkaline phosphatase.
 5. Thekit of claim 1, wherein the kit contains antibodies that detect no morethan three polypeptide determinants.